A novel broadcast encryption scheme for group communication scenarios in distributed networks is presented. In the scheme, anyone is allowed to encrypt a message and distribute it to a designated group. Each member in...A novel broadcast encryption scheme for group communication scenarios in distributed networks is presented. In the scheme, anyone is allowed to encrypt a message and distribute it to a designated group. Each member in the designated group has the ability to independently decrypt a ciphertext. In contrast to traditional broadcast encryption, all the valid receivers in the proposed scheme compose the designated group. To take advantage of this property, a tab for the group is set and the matching private key for each member is generated. In addition, before decrypting a ciphertext, anyone in the scheme can verify the ciphertext, to ensure that the ciphertext is correct. This property is very important for large-scale group communication, as the gateway can filter incorrect ciphertext and alleviate the receiver＇s workload. Finally, a proof in the random oracle model is given, to show that the proposed scheme is secure against the adaptively chosen ciphertext attack.展开更多
当今科技飞速发展,隐私保护成为一个重要议题.为了确保数据的安全性,通常选择将数据加密后存储在云服务器上,然而这样云服务器无法对加密后的数据进行计算、统计等有效处理,从而使得很多应用场景受限.为了解决这个问题,提出一种基于环...当今科技飞速发展,隐私保护成为一个重要议题.为了确保数据的安全性,通常选择将数据加密后存储在云服务器上,然而这样云服务器无法对加密后的数据进行计算、统计等有效处理,从而使得很多应用场景受限.为了解决这个问题,提出一种基于环上容错学习(ring learning with error,R-LWE)问题的PKE-MET(public-key encryption with a multiple-ciphertext equality test)方案,并给出了正确性和安全性分析.该方案允许云服务器同时对多个密文执行相等性测试,还能够抵抗量子计算攻击.基于Palisade库对方案进行了实现,从理论与实现的角度与其他方案进行了比较分析.相较于其他方案,该方案具有高效、运行时间短的优点.展开更多
在定位请求服务中,如何保护用户的位置隐私和位置服务提供商(Localization service provider,LSP)的数据隐私是关系到WiFi指纹定位应用的一个具有挑战性的问题。基于密文域的K-近邻(K-nearest neighbors,KNN)检索,本文提出了一种适用于...在定位请求服务中,如何保护用户的位置隐私和位置服务提供商(Localization service provider,LSP)的数据隐私是关系到WiFi指纹定位应用的一个具有挑战性的问题。基于密文域的K-近邻(K-nearest neighbors,KNN)检索,本文提出了一种适用于三方的定位隐私保护算法,能有效提升对LSP指纹信息隐私的保护强度并降低计算开销。服务器和用户分别完成对指纹信息和定位请求的加密,而第三方则基于加密指纹库和加密定位请求,在隐私状态下完成对用户的位置估计。所提算法把各参考点的位置信息随机嵌入指纹,可避免恶意用户获取各参考点的具体位置;进一步利用布隆滤波器在隐藏接入点信息的情况下,第三方可完成参考点的在线匹配,实现对用户隐私状态下的粗定位,可与定位算法结合降低计算开销。在公共数据集和实验室数据集中,对两种算法的安全、开销和定位性能进行了全面的评估。与同类加密算法比较,在不降低定位精度的情况下,进一步增强了对数据隐私的保护。展开更多
文摘A novel broadcast encryption scheme for group communication scenarios in distributed networks is presented. In the scheme, anyone is allowed to encrypt a message and distribute it to a designated group. Each member in the designated group has the ability to independently decrypt a ciphertext. In contrast to traditional broadcast encryption, all the valid receivers in the proposed scheme compose the designated group. To take advantage of this property, a tab for the group is set and the matching private key for each member is generated. In addition, before decrypting a ciphertext, anyone in the scheme can verify the ciphertext, to ensure that the ciphertext is correct. This property is very important for large-scale group communication, as the gateway can filter incorrect ciphertext and alleviate the receiver＇s workload. Finally, a proof in the random oracle model is given, to show that the proposed scheme is secure against the adaptively chosen ciphertext attack.
文摘当今科技飞速发展,隐私保护成为一个重要议题.为了确保数据的安全性,通常选择将数据加密后存储在云服务器上,然而这样云服务器无法对加密后的数据进行计算、统计等有效处理,从而使得很多应用场景受限.为了解决这个问题,提出一种基于环上容错学习(ring learning with error,R-LWE)问题的PKE-MET(public-key encryption with a multiple-ciphertext equality test)方案,并给出了正确性和安全性分析.该方案允许云服务器同时对多个密文执行相等性测试,还能够抵抗量子计算攻击.基于Palisade库对方案进行了实现,从理论与实现的角度与其他方案进行了比较分析.相较于其他方案,该方案具有高效、运行时间短的优点.
文摘在定位请求服务中,如何保护用户的位置隐私和位置服务提供商(Localization service provider,LSP)的数据隐私是关系到WiFi指纹定位应用的一个具有挑战性的问题。基于密文域的K-近邻(K-nearest neighbors,KNN)检索,本文提出了一种适用于三方的定位隐私保护算法,能有效提升对LSP指纹信息隐私的保护强度并降低计算开销。服务器和用户分别完成对指纹信息和定位请求的加密,而第三方则基于加密指纹库和加密定位请求,在隐私状态下完成对用户的位置估计。所提算法把各参考点的位置信息随机嵌入指纹,可避免恶意用户获取各参考点的具体位置;进一步利用布隆滤波器在隐藏接入点信息的情况下,第三方可完成参考点的在线匹配,实现对用户隐私状态下的粗定位,可与定位算法结合降低计算开销。在公共数据集和实验室数据集中,对两种算法的安全、开销和定位性能进行了全面的评估。与同类加密算法比较,在不降低定位精度的情况下,进一步增强了对数据隐私的保护。
