With the promotion of digital currency,how to effectively solve the authenticity,privacy and usability of digital currency issuance has been a key problem.Redactable signature scheme(RSS)can provide the verification o...With the promotion of digital currency,how to effectively solve the authenticity,privacy and usability of digital currency issuance has been a key problem.Redactable signature scheme(RSS)can provide the verification of the integrity and source of the generated sub-documents and solve the privacy problem in digital currency by removing blocks from the signed documents.Unfortunately,it has not realized the consolidation of signed documents,which can not solve the problem of merging two digital currencies.Now,we introduce the concept of weight based on the threshold secret sharing scheme(TSSS)and present a redactable signature scheme with merge algorithm(RSS-MA)using the quasi-commutative accumulator.Our scheme can reduce the communication overhead by utilizing the merge algorithm when transmitting multiple digital currency signatures.Furthermore,this can effectively hide the scale of users’private monetary assets and the number of transactions between users.While meeting the three properties of digital currency issuance,in order to ensure the availability of digital currency after redacting,editors shall not remove the relevant identification information block form digital currency.Finally,our security proof and the analysis of efficiency show that RSS-MA greatly improves the communication and computation efficiency when transmitting multiple signatures.展开更多
With the growth of requirements for data sharing,a novel business model of digital assets trading has emerged that allows data owners to sell their data for monetary gain.In the distributed ledger of blockchain,howeve...With the growth of requirements for data sharing,a novel business model of digital assets trading has emerged that allows data owners to sell their data for monetary gain.In the distributed ledger of blockchain,however,the privacy of stakeholder's identity and the confidentiality of data content are threatened.Therefore,we proposed a blockchainenabled privacy-preserving and access control scheme to address the above problems.First,the multi-channel mechanism is introduced to provide the privacy protection of distributed ledger inside the channel and achieve coarse-grained access control to digital assets.Then,we use multi-authority attribute-based encryption(MAABE)algorithm to build a fine-grained access control model for data trading in a single channel and describe its instantiation in detail.Security analysis shows that the scheme has IND-CPA secure and can provide privacy protection and collusion resistance.Compared with other schemes,our solution has better performance in privacy protection and access control.The evaluation results demonstrate its effectiveness and practicability.展开更多
With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for clou...With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis.展开更多
Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The ris...Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.展开更多
As an important branch of federated learning,vertical federated learning(VFL)enables multiple institutions to train on the same user samples,bringing considerable industry benefits.However,VFL needs to exchange user f...As an important branch of federated learning,vertical federated learning(VFL)enables multiple institutions to train on the same user samples,bringing considerable industry benefits.However,VFL needs to exchange user features among multiple institutions,which raises concerns about privacy leakage.Moreover,existing multi-party VFL privacy-preserving schemes suffer from issues such as poor reli-ability and high communication overhead.To address these issues,we propose a privacy protection scheme for four institutional VFLs,named FVFL.A hierarchical framework is first introduced to support federated training among four institutions.We also design a verifiable repli-cated secret sharing(RSS)protocol(32)-sharing and combine it with homomorphic encryption to ensure the reliability of FVFL while ensuring the privacy of features and intermediate results of the four institutions.Our theoretical analysis proves the reliability and security of the pro-posed FVFL.Extended experiments verify that the proposed scheme achieves excellent performance with a low communication overhead.展开更多
With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT t...With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain,but edge computing could solve this problem.The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity.However,user data in edge computing is usually stored and processed in some honest-but-curious authorized entities,which leads to the leakage of users’privacy information.In order to solve these problems,this paper proposes a location data collection method that satisfies the local differential privacy to protect users’privacy.In this paper,a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located.A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid.In addition,the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments.Compared with the existing privacy-preserving methods,the proposed privacy-preserving mechanism can not only better meet users’privacy needs,but also have higher data availability.展开更多
Chinese culture is featured by its "togetherness", collectivism, and its agricultural tradition; while American culture is featured by its "apartness", individualism and its industrial tradition. T...Chinese culture is featured by its "togetherness", collectivism, and its agricultural tradition; while American culture is featured by its "apartness", individualism and its industrial tradition. The three dominant features determine the two cultures' different privacy rules. This paper puts the focus on the analysis of the causes behind the two different privacy. What's more, the globalization influences the privacy rules of the two cultures, people are more adaptive and have changed a lot.展开更多
Mobile edge computing(MEC)is an emerging technolohgy that extends cloud computing to the edge of a network.MEC has been applied to a variety of services.Specially,MEC can help to reduce network delay and improve the s...Mobile edge computing(MEC)is an emerging technolohgy that extends cloud computing to the edge of a network.MEC has been applied to a variety of services.Specially,MEC can help to reduce network delay and improve the service quality of recommendation systems.In a MEC-based recommendation system,users’rating data are collected and analyzed by the edge servers.If the servers behave dishonestly or break down,users’privacy may be disclosed.To solve this issue,we design a recommendation framework that applies local differential privacy(LDP)to collaborative filtering.In the proposed framework,users’rating data are perturbed to satisfy LDP and then released to the edge servers.The edge servers perform partial computing task by using the perturbed data.The cloud computing center computes the similarity between items by using the computing results generated by edge servers.We propose a data perturbation method to protect user’s original rating values,where the Harmony mechanism is modified so as to preserve the accuracy of similarity computation.And to enhance the protection of privacy,we propose two methods to protect both users’rating values and rating behaviors.Experimental results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommendation methods.展开更多
User profile matching can establish social relationships between different users in the social network.If the user profile is matched in plaintext,the user's privacy might face a security challenge.Although there ...User profile matching can establish social relationships between different users in the social network.If the user profile is matched in plaintext,the user's privacy might face a security challenge.Although there exist some schemes realizing privacypreserving user profile matching,the resource-limited users or social service providers in these schemes need to take higher computational complexity to ensure the privacy or matching of the data.To overcome the problems,a novel privacy-preserving user profile matching protocol in social networks is proposed by using t-out-of n servers and the bloom filter technique,in which the computational complexity of a user is reduced by applying the Chinese Remainder Theorem,the matching users can be found with the help of any t matching servers,and the privacy of the user profile is not compromised.Furthermore,if at most t-1 servers are allowed to collude,our scheme can still fulfill user profile privacy and user query privacy.Finally,the performance of the proposed scheme is compared with the other two schemes,and the results show that our scheme is superior to them.展开更多
There are a lot of personal information stored in our smartphones, for instance, contacts, messages, photos, banking credentials and social network access. Therefore, ensuring personal data safety is a critical resear...There are a lot of personal information stored in our smartphones, for instance, contacts, messages, photos, banking credentials and social network access. Therefore, ensuring personal data safety is a critical research and practical issue. The objective of this paper is to evaluate the influence of personal data sect,rity and decrease the privacy risks in the Android system. We apply the concept of privacy impact assessment （PIA） to design a system, which identifies permission requirements of apps, detects the potential activities from the logger and analyses the configuration settings. The system provides a user-friendly interface for users to get in-depth knowledge of the impact of privacy risk, and it could run on Android devices without USB teleport and network connection to avoid other problems. Our research finds that many apps announce numerous unnecessary permissions, and the application installing confirmation dialog does not show all requirement permissions when apps are installed first time.展开更多
The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Neverth...The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Nevertheless,collecting raw data,which may contain various per⁃sonal information,would lead to serious personal privacy leaks.Local differential privacy(LDP)has been proposed to protect privacy on the device side so that the server cannot obtain the raw data.However,existing mechanisms assume that all keys are equally sensitive,which can⁃not produce high-precision statistical results.A utility-improved data collection framework with LDP for key-value formed mobile data is pro⁃posed to solve this issue.More specifically,we divide the key-value data into sensitive and non-sensitive parts and only provide an LDPequivalent privacy guarantee for sensitive keys and all values.We instantiate our framework by using a utility-improved key value-unary en⁃coding(UKV-UE)mechanism based on unary encoding,with which our framework can work effectively for a large key domain.We then vali⁃date our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets.Finally,some pos⁃sible future research directions are envisioned.展开更多
As a kind of cultural phenomena, privacy has different meaning in different culture. In the intercultural communication, it is very important to have a correct view of privacy. This thesis compares and analyzes the pr...As a kind of cultural phenomena, privacy has different meaning in different culture. In the intercultural communication, it is very important to have a correct view of privacy. This thesis compares and analyzes the privacy between China and the west in diverse angles. Based on the western scholar, privacy is a selective control of access to self or to one's group. Knowing the concept of privacy and the way to face the culture shock which followed by different view of privacy, we find some important things. That is, with the globalization of modern society, the common understanding on privacy will be more and the problems in the intercultural communication will be fewer if we understand and respect the other culture.展开更多
Wireless transmission method in wireless sensor networks has put forward higher requirements for private protection technology. According to the packet loss problem of private protection algorithm based on slice techn...Wireless transmission method in wireless sensor networks has put forward higher requirements for private protection technology. According to the packet loss problem of private protection algorithm based on slice technology, this paper proposes the data private protection algorithm with redundancy mechanism, which ensures privacy by privacy homomorphism mechanism and guarantees redundancy by carrying hidden data. Moreover,it selects the routing tree generated by CTP(Collection Tree Protocol) as routing path for data transmission. By dividing at the source node, it adds the hidden information and also the privacy homomorphism. At the same time,the information feedback tree is established between the destination node and the source node. In addition, the destination node immediately sends the packet loss information and the encryption key via the information feedback tree to the source node. As a result,it improves the reliability and privacy of data transmission and ensures the data redundancy.展开更多
Location-based services(LBS)in vehicular ad hoc networks(VANETs)must protect users’privacy and address the threat of the exposure of sensitive locations during LBS requests.Users release not only their geographical b...Location-based services(LBS)in vehicular ad hoc networks(VANETs)must protect users’privacy and address the threat of the exposure of sensitive locations during LBS requests.Users release not only their geographical but also semantic information of the visited places(e.g.,hospital).This sensitive information enables the inference attacker to exploit the users’preferences and life patterns.In this paper we propose a reinforcement learning(RL)based sensitive semantic location privacy protection scheme.This scheme uses the idea of differential privacy to randomize the released vehicle locations and adaptively selects the perturbation policy based on the sensitivity of the semantic location and the attack history.This scheme enables a vehicle to optimize the perturbation policy in terms of the privacy and the quality of service(QoS)loss without being aware of the current inference attack model in a dynamic privacy protection process.To solve the location protection problem with high-dimensional and continuous-valued perturbation policy variables,a deep deterministic policy gradientbased semantic location perturbation scheme(DSLP)is developed.The actor part is used to generate continuous privacy budget and perturbation angle,and the critic part is used to estimate the performance of the policy.Simulations demonstrate the DSLP-based scheme outperforms the benchmark schemes,which increases the privacy,reduces the QoS loss,and increases the utility of the vehicle.展开更多
Privacy-preserving data publishing （PPDP） is one of the hot issues in the field of the network security. The existing PPDP technique cannot deal with generality attacks, which explicitly contain the sensitivity atta...Privacy-preserving data publishing （PPDP） is one of the hot issues in the field of the network security. The existing PPDP technique cannot deal with generality attacks, which explicitly contain the sensitivity attack and the similarity attack. This paper proposes a novel model, （w,γ, k）-anonymity, to avoid generality attacks on both cases of numeric and categorical attributes. We show that the optimal （w, γ, k）-anonymity problem is NP-hard and conduct the Top-down Local recoding （TDL） algorithm to implement the model. Our experiments validate the improvement of our model with real data.展开更多
A Service Level Agreement(SLA) is a legal contract between any two parties to ensure an adequate Quality of Service(Qo S). Most research on SLAs has concentrated on protecting the user data through encryption. However...A Service Level Agreement(SLA) is a legal contract between any two parties to ensure an adequate Quality of Service(Qo S). Most research on SLAs has concentrated on protecting the user data through encryption. However, these methods can not supervise a cloud service provider(CSP) directly. In order to address this problem, we propose a privacy-based SLA violation detection model for cloud computing based on Markov decision process theory. This model can recognize and regulate CSP's actions based on specific requirements of various users. Additionally, the model could make effective evaluation to the credibility of CSP, and can monitor events that user privacy is violated. Experiments and analysis indicate that the violation detection model can achieve good results in both the algorithm's convergence and prediction effect.展开更多
Post-processing is indispensable in quantum key distribution （QKD）, which is aimed at sharing secret keys between two distant parties. It mainly consists of key reconciliation and privacy amplification, which is use...Post-processing is indispensable in quantum key distribution （QKD）, which is aimed at sharing secret keys between two distant parties. It mainly consists of key reconciliation and privacy amplification, which is used for sharing the same keys and for distilling unconditional secret keys. In this paper, we focus on speeding up the privacy amplification process by choosing a simple multiplicative universal class of hash functions. By constructing an optimal multiplication algorithm based on four basic multiplication algorithms, we give a fast software implementation of length-adaptive privacy amplification. ＂Length-adaptive＂ indicates that the implementation of privacy amplification automatically adapts to different lengths of input blocks. When the lengths of the input blocks are 1 Mbit and 10 Mbit, the speed of privacy amplification can be as fast as 14.86 Mbps and 10.88 Mbps, respectively. Thus, it is practical for GHz or even higher repetition frequency QKD systems.展开更多
The blockchain technology has been applied to wide areas.However,the open and transparent properties of the blockchains pose serious challenges to users’privacy.Among all the schemes for the privacy protection,the ze...The blockchain technology has been applied to wide areas.However,the open and transparent properties of the blockchains pose serious challenges to users’privacy.Among all the schemes for the privacy protection,the zero-knowledge proof algorithm conceals most of the private information in a transaction,while participants of the blockchain can validate this transaction without the private information.However,current schemes are only aimed at blockchains with the UTXO model,and only one type of assets circulates on these blockchains.Based on the zero-knowledge proof algorithm,this paper proposes a privacy protection scheme for blockchains that use the account and multi-asset model.We design the transaction structure,anonymous addresses and anonymous asset metadata,and also propose the methods of the asset transfer and double-spending detection.The zk-SNARKs algorithm is used to generate and to verify the zero-knowledge proof.And finally,we conduct the experiments to evaluate our scheme.展开更多
Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic management.Meanwhile,the main concerns have been raised abou...Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic management.Meanwhile,the main concerns have been raised about the security and privacy of vehicle data.The mobility and real-time characteristics of vehicle data make data sharing more difficult in Io V.The emergence of blockchain and federated learning brings new directions.In this paper,a data-sharing model that combines blockchain and federated learning is proposed to solve the security and privacy problems of data sharing in Io V.First,we use federated learning to share data instead of exposing actual data and propose an adaptive differential privacy scheme to further balance the privacy and availability of data.Then,we integrate the verification scheme into the consensus process,so that the consensus computation can filter out low-quality models.Experimental data shows that our data-sharing model can better balance the relationship between data availability and privacy,and also has enhanced security.展开更多
With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issu...With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection(PS-ACS). In the PS-ACS scheme, we divide users into private domain(PRD) and public domain(PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption(KAE) and the Improved Attribute-based Signature(IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption(CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users' privacy in cloud-based services.展开更多
基金supported by Support Plan of Scientific and Technological Innovation Team in Universities of Henan Province(20IRTSTHN013)Shaanxi Key Laboratory of Information Communication Network and Security,Xi’an University of Posts&Telecommunications,Xi’an,Shaanxi 710121,China(ICNS202006)The National Natural Science Fund(No.61802117).
文摘With the promotion of digital currency,how to effectively solve the authenticity,privacy and usability of digital currency issuance has been a key problem.Redactable signature scheme(RSS)can provide the verification of the integrity and source of the generated sub-documents and solve the privacy problem in digital currency by removing blocks from the signed documents.Unfortunately,it has not realized the consolidation of signed documents,which can not solve the problem of merging two digital currencies.Now,we introduce the concept of weight based on the threshold secret sharing scheme(TSSS)and present a redactable signature scheme with merge algorithm(RSS-MA)using the quasi-commutative accumulator.Our scheme can reduce the communication overhead by utilizing the merge algorithm when transmitting multiple digital currency signatures.Furthermore,this can effectively hide the scale of users’private monetary assets and the number of transactions between users.While meeting the three properties of digital currency issuance,in order to ensure the availability of digital currency after redacting,editors shall not remove the relevant identification information block form digital currency.Finally,our security proof and the analysis of efficiency show that RSS-MA greatly improves the communication and computation efficiency when transmitting multiple signatures.
基金supported by National Key Research and Development Plan in China(Grant No.2020YFB1005500)Beijing Natural Science Foundation(Grant No.M21034)BUPT Excellent Ph.D Students Foundation(Grant No.CX2023218)。
文摘With the growth of requirements for data sharing,a novel business model of digital assets trading has emerged that allows data owners to sell their data for monetary gain.In the distributed ledger of blockchain,however,the privacy of stakeholder's identity and the confidentiality of data content are threatened.Therefore,we proposed a blockchainenabled privacy-preserving and access control scheme to address the above problems.First,the multi-channel mechanism is introduced to provide the privacy protection of distributed ledger inside the channel and achieve coarse-grained access control to digital assets.Then,we use multi-authority attribute-based encryption(MAABE)algorithm to build a fine-grained access control model for data trading in a single channel and describe its instantiation in detail.Security analysis shows that the scheme has IND-CPA secure and can provide privacy protection and collusion resistance.Compared with other schemes,our solution has better performance in privacy protection and access control.The evaluation results demonstrate its effectiveness and practicability.
基金sponsored by the National Natural Science Foundation of China under grant number No. 62172353, No. 62302114, No. U20B2046 and No. 62172115Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education No.1331007 and No. 1311022+1 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions Grant No. 17KJB520044Six Talent Peaks Project in Jiangsu Province No.XYDXX-108
文摘With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis.
基金supported in part by the National Key Research and Development Program of China under 2020AAA0106000the National Natural Science Foundation of China under U20B2060 and U21B2036supported by a grant from the Guoqiang Institute, Tsinghua University under 2021GQG1005
文摘Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.
基金supported in part by ZTE Industry-University-Institute Cooperation Funds under Grant No. 202211FKY00112Open Research Projects of Zhejiang Lab under Grant No. 2022QA0AB02Natural Science Foundation of Sichuan Province under Grant No. 2022NSFSC0913
文摘As an important branch of federated learning,vertical federated learning(VFL)enables multiple institutions to train on the same user samples,bringing considerable industry benefits.However,VFL needs to exchange user features among multiple institutions,which raises concerns about privacy leakage.Moreover,existing multi-party VFL privacy-preserving schemes suffer from issues such as poor reli-ability and high communication overhead.To address these issues,we propose a privacy protection scheme for four institutional VFLs,named FVFL.A hierarchical framework is first introduced to support federated training among four institutions.We also design a verifiable repli-cated secret sharing(RSS)protocol(32)-sharing and combine it with homomorphic encryption to ensure the reliability of FVFL while ensuring the privacy of features and intermediate results of the four institutions.Our theoretical analysis proves the reliability and security of the pro-posed FVFL.Extended experiments verify that the proposed scheme achieves excellent performance with a low communication overhead.
文摘With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain,but edge computing could solve this problem.The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity.However,user data in edge computing is usually stored and processed in some honest-but-curious authorized entities,which leads to the leakage of users’privacy information.In order to solve these problems,this paper proposes a location data collection method that satisfies the local differential privacy to protect users’privacy.In this paper,a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located.A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid.In addition,the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments.Compared with the existing privacy-preserving methods,the proposed privacy-preserving mechanism can not only better meet users’privacy needs,but also have higher data availability.
文摘Chinese culture is featured by its "togetherness", collectivism, and its agricultural tradition; while American culture is featured by its "apartness", individualism and its industrial tradition. The three dominant features determine the two cultures' different privacy rules. This paper puts the focus on the analysis of the causes behind the two different privacy. What's more, the globalization influences the privacy rules of the two cultures, people are more adaptive and have changed a lot.
基金supported by National Natural Science Foundation of China(No.61871037)supported by Natural Science Foundation of Beijing(No.M21035).
文摘Mobile edge computing(MEC)is an emerging technolohgy that extends cloud computing to the edge of a network.MEC has been applied to a variety of services.Specially,MEC can help to reduce network delay and improve the service quality of recommendation systems.In a MEC-based recommendation system,users’rating data are collected and analyzed by the edge servers.If the servers behave dishonestly or break down,users’privacy may be disclosed.To solve this issue,we design a recommendation framework that applies local differential privacy(LDP)to collaborative filtering.In the proposed framework,users’rating data are perturbed to satisfy LDP and then released to the edge servers.The edge servers perform partial computing task by using the perturbed data.The cloud computing center computes the similarity between items by using the computing results generated by edge servers.We propose a data perturbation method to protect user’s original rating values,where the Harmony mechanism is modified so as to preserve the accuracy of similarity computation.And to enhance the protection of privacy,we propose two methods to protect both users’rating values and rating behaviors.Experimental results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommendation methods.
基金supported in part by the Natural Science Foundation of Beijing(no.4212019,M22002)the National Natural Science Foundation of China(no.62172005)+1 种基金the Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province(No.ZCL21014)the Foundation of Guizhou Provincial Key Laboratory of Public Big Data(no.2019BDKF JJ012)。
文摘User profile matching can establish social relationships between different users in the social network.If the user profile is matched in plaintext,the user's privacy might face a security challenge.Although there exist some schemes realizing privacypreserving user profile matching,the resource-limited users or social service providers in these schemes need to take higher computational complexity to ensure the privacy or matching of the data.To overcome the problems,a novel privacy-preserving user profile matching protocol in social networks is proposed by using t-out-of n servers and the bloom filter technique,in which the computational complexity of a user is reduced by applying the Chinese Remainder Theorem,the matching users can be found with the help of any t matching servers,and the privacy of the user profile is not compromised.Furthermore,if at most t-1 servers are allowed to collude,our scheme can still fulfill user profile privacy and user query privacy.Finally,the performance of the proposed scheme is compared with the other two schemes,and the results show that our scheme is superior to them.
基金supported in part by the Ministry of Science and Technology of Taiwan,China under Grant No.MOST 102-2221-E-017-003-MY3
文摘There are a lot of personal information stored in our smartphones, for instance, contacts, messages, photos, banking credentials and social network access. Therefore, ensuring personal data safety is a critical research and practical issue. The objective of this paper is to evaluate the influence of personal data sect,rity and decrease the privacy risks in the Android system. We apply the concept of privacy impact assessment （PIA） to design a system, which identifies permission requirements of apps, detects the potential activities from the logger and analyses the configuration settings. The system provides a user-friendly interface for users to get in-depth knowledge of the impact of privacy risk, and it could run on Android devices without USB teleport and network connection to avoid other problems. Our research finds that many apps announce numerous unnecessary permissions, and the application installing confirmation dialog does not show all requirement permissions when apps are installed first time.
文摘The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Nevertheless,collecting raw data,which may contain various per⁃sonal information,would lead to serious personal privacy leaks.Local differential privacy(LDP)has been proposed to protect privacy on the device side so that the server cannot obtain the raw data.However,existing mechanisms assume that all keys are equally sensitive,which can⁃not produce high-precision statistical results.A utility-improved data collection framework with LDP for key-value formed mobile data is pro⁃posed to solve this issue.More specifically,we divide the key-value data into sensitive and non-sensitive parts and only provide an LDPequivalent privacy guarantee for sensitive keys and all values.We instantiate our framework by using a utility-improved key value-unary en⁃coding(UKV-UE)mechanism based on unary encoding,with which our framework can work effectively for a large key domain.We then vali⁃date our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets.Finally,some pos⁃sible future research directions are envisioned.
文摘As a kind of cultural phenomena, privacy has different meaning in different culture. In the intercultural communication, it is very important to have a correct view of privacy. This thesis compares and analyzes the privacy between China and the west in diverse angles. Based on the western scholar, privacy is a selective control of access to self or to one's group. Knowing the concept of privacy and the way to face the culture shock which followed by different view of privacy, we find some important things. That is, with the globalization of modern society, the common understanding on privacy will be more and the problems in the intercultural communication will be fewer if we understand and respect the other culture.
基金sponsored by the National Key R&D Program of China(No.2018YFB1003201)the National Natural Science Foundation of China(No.61672296,No.61602261)Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province(No.18KJA520008)
文摘Wireless transmission method in wireless sensor networks has put forward higher requirements for private protection technology. According to the packet loss problem of private protection algorithm based on slice technology, this paper proposes the data private protection algorithm with redundancy mechanism, which ensures privacy by privacy homomorphism mechanism and guarantees redundancy by carrying hidden data. Moreover,it selects the routing tree generated by CTP(Collection Tree Protocol) as routing path for data transmission. By dividing at the source node, it adds the hidden information and also the privacy homomorphism. At the same time,the information feedback tree is established between the destination node and the source node. In addition, the destination node immediately sends the packet loss information and the encryption key via the information feedback tree to the source node. As a result,it improves the reliability and privacy of data transmission and ensures the data redundancy.
基金This work was supported in part by National Natural Science Foundation of China under Grant 61971366 and 61771474,and in part by the Fundamental Research Funds for the central universities No.20720200077,and in part by Major Science and Technology Innovation Projects of Shandong Province 2019JZZY020505 and Key R&D Projects of Xuzhou City KC18171,and in part by NSF EARS-1839818,CNS1717454,CNS-1731424,and CNS-1702850.
文摘Location-based services(LBS)in vehicular ad hoc networks(VANETs)must protect users’privacy and address the threat of the exposure of sensitive locations during LBS requests.Users release not only their geographical but also semantic information of the visited places(e.g.,hospital).This sensitive information enables the inference attacker to exploit the users’preferences and life patterns.In this paper we propose a reinforcement learning(RL)based sensitive semantic location privacy protection scheme.This scheme uses the idea of differential privacy to randomize the released vehicle locations and adaptively selects the perturbation policy based on the sensitivity of the semantic location and the attack history.This scheme enables a vehicle to optimize the perturbation policy in terms of the privacy and the quality of service(QoS)loss without being aware of the current inference attack model in a dynamic privacy protection process.To solve the location protection problem with high-dimensional and continuous-valued perturbation policy variables,a deep deterministic policy gradientbased semantic location perturbation scheme(DSLP)is developed.The actor part is used to generate continuous privacy budget and perturbation angle,and the critic part is used to estimate the performance of the policy.Simulations demonstrate the DSLP-based scheme outperforms the benchmark schemes,which increases the privacy,reduces the QoS loss,and increases the utility of the vehicle.
基金supported in part by Research Fund for the Doctoral Program of Higher Education of China(No.20120009110007)Program for Innovative Research Team in University of Ministry of Education of China (No.IRT201206)+3 种基金Program for New Century Excellent Talents in University(NCET-110565)the Fundamental Research Funds for the Central Universities(No.2012JBZ010)the Open Project Program of Beijing Key Laboratory of Trusted Computing at Beijing University of TechnologyBeijing Higher Education Young Elite Teacher Project(No. YETP0542)
文摘Privacy-preserving data publishing （PPDP） is one of the hot issues in the field of the network security. The existing PPDP technique cannot deal with generality attacks, which explicitly contain the sensitivity attack and the similarity attack. This paper proposes a novel model, （w,γ, k）-anonymity, to avoid generality attacks on both cases of numeric and categorical attributes. We show that the optimal （w, γ, k）-anonymity problem is NP-hard and conduct the Top-down Local recoding （TDL） algorithm to implement the model. Our experiments validate the improvement of our model with real data.
基金supported in part by National Natural Science Foundation of China (NSFC) under Grant U1509219 and 2017YFB0802900
文摘A Service Level Agreement(SLA) is a legal contract between any two parties to ensure an adequate Quality of Service(Qo S). Most research on SLAs has concentrated on protecting the user data through encryption. However, these methods can not supervise a cloud service provider(CSP) directly. In order to address this problem, we propose a privacy-based SLA violation detection model for cloud computing based on Markov decision process theory. This model can recognize and regulate CSP's actions based on specific requirements of various users. Additionally, the model could make effective evaluation to the credibility of CSP, and can monitor events that user privacy is violated. Experiments and analysis indicate that the violation detection model can achieve good results in both the algorithm's convergence and prediction effect.
基金supported by the National Basic Research Program of China(Grant Nos.2011CBA00200 and 2011CB921200)the National Natural Science Foundation of China(Grant Nos.60921091 and 61101137)
文摘Post-processing is indispensable in quantum key distribution （QKD）, which is aimed at sharing secret keys between two distant parties. It mainly consists of key reconciliation and privacy amplification, which is used for sharing the same keys and for distilling unconditional secret keys. In this paper, we focus on speeding up the privacy amplification process by choosing a simple multiplicative universal class of hash functions. By constructing an optimal multiplication algorithm based on four basic multiplication algorithms, we give a fast software implementation of length-adaptive privacy amplification. ＂Length-adaptive＂ indicates that the implementation of privacy amplification automatically adapts to different lengths of input blocks. When the lengths of the input blocks are 1 Mbit and 10 Mbit, the speed of privacy amplification can be as fast as 14.86 Mbps and 10.88 Mbps, respectively. Thus, it is practical for GHz or even higher repetition frequency QKD systems.
基金supported by National Natural Science Foundation of China(61672499,61772502)Key Special Project of Beijing Municipal Science&Technology Commission(Z181100003218018)+1 种基金Natural Science Foundation of Inner Mongolia,Open Foundation of State key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications,SKLNST-2016-2-09)SV-ICT Blockchain&DAPP Joint Lab
文摘The blockchain technology has been applied to wide areas.However,the open and transparent properties of the blockchains pose serious challenges to users’privacy.Among all the schemes for the privacy protection,the zero-knowledge proof algorithm conceals most of the private information in a transaction,while participants of the blockchain can validate this transaction without the private information.However,current schemes are only aimed at blockchains with the UTXO model,and only one type of assets circulates on these blockchains.Based on the zero-knowledge proof algorithm,this paper proposes a privacy protection scheme for blockchains that use the account and multi-asset model.We design the transaction structure,anonymous addresses and anonymous asset metadata,and also propose the methods of the asset transfer and double-spending detection.The zk-SNARKs algorithm is used to generate and to verify the zero-knowledge proof.And finally,we conduct the experiments to evaluate our scheme.
基金supported by the Ministry of Education Industry-University Cooperation Collaborative Education Projects of China under Grant 202102119036 and 202102082013。
文摘Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic management.Meanwhile,the main concerns have been raised about the security and privacy of vehicle data.The mobility and real-time characteristics of vehicle data make data sharing more difficult in Io V.The emergence of blockchain and federated learning brings new directions.In this paper,a data-sharing model that combines blockchain and federated learning is proposed to solve the security and privacy problems of data sharing in Io V.First,we use federated learning to share data instead of exposing actual data and propose an adaptive differential privacy scheme to further balance the privacy and availability of data.Then,we integrate the verification scheme into the consensus process,so that the consensus computation can filter out low-quality models.Experimental data shows that our data-sharing model can better balance the relationship between data availability and privacy,and also has enhanced security.
基金financially supported by the National Natural Science Foundation of China(No.61303216,No.61272457,No.U1401251,and No.61373172)the National High Technology Research and Development Program of China(863 Program)(No.2012AA013102)National 111 Program of China B16037 and B08038
文摘With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection(PS-ACS). In the PS-ACS scheme, we divide users into private domain(PRD) and public domain(PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption(KAE) and the Improved Attribute-based Signature(IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption(CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users' privacy in cloud-based services.
