The Internet now is a large-scale platform with big data. Finding truth from a huge dataset has attracted extensive attention, which can maintain the quality of data collected by users and provide users with accurate ...The Internet now is a large-scale platform with big data. Finding truth from a huge dataset has attracted extensive attention, which can maintain the quality of data collected by users and provide users with accurate and efficient data. However, current truth finder algorithms are unsatisfying, because of their low accuracy and complication. This paper proposes a truth finder algorithm based on entity attributes (TFAEA). Based on the iterative computation of source reliability and fact accuracy, TFAEA considers the interactive degree among facts and the degree of dependence among sources, to simplify the typical truth finder algorithms. In order to improve the accuracy of them, TFAEA combines the one-way text similarity and the factual conflict to calculate the mutual support degree among facts. Furthermore, TFAEA utilizes the symmetric saturation of data sources to calculate the degree of dependence among sources. The experimental results show that TFAEA is not only more stable, but also more accurate than the typical truth finder algorithms.展开更多
在群智感知系统中,用户通过提供感知数据完成感知任务.然而,由于传感设备精度、用户行为以及环境条件等因素的影响,不同用户提供的数据质量存在显著差异.真值发现技术能够有效地消除低质量数据影响,从而能够更好地利用感知数据,但现有...在群智感知系统中,用户通过提供感知数据完成感知任务.然而,由于传感设备精度、用户行为以及环境条件等因素的影响,不同用户提供的数据质量存在显著差异.真值发现技术能够有效地消除低质量数据影响,从而能够更好地利用感知数据,但现有的真值发现方法往往忽略用户个性化隐私要求,且基于加密的隐私保护技术难以应用于大量用户参与的群智感知系统.基于此,提出一种个性化差分隐私真值发现(personalized differential privacy truth discovery,PDPTD)方法.PDPTD将本地差分隐私随机响应机制应用于隐私保护,使用户能够根据个人隐私需求自主调整感知数据的扰动幅度,平衡数据隐私与可用性.服务器在聚合数据时充分考虑扰动影响,并通过加权机制提升数据质量.与此同时,PDPTD依据数据质量动态分配用户权重,即使部分用户选择较高程度的扰动,系统仍能推断出接近真实值的结果,从而保证数据的可靠性.理论分析与实验结果表明,PDPTD方案符合本地差分隐私原则,同时确保最终推断结果具有较高的精确度.展开更多
隐私保护真值发现技术在移动群智感知网络领域中受到了广泛关注.然而在实际应用中,恶意用户上传的异常值对真值发现结果的可靠性带来了较大影响.为此,提出了一种基于区间验证的隐私保护真值发现算法IVPPTD (Interval Verification based...隐私保护真值发现技术在移动群智感知网络领域中受到了广泛关注.然而在实际应用中,恶意用户上传的异常值对真值发现结果的可靠性带来了较大影响.为此,提出了一种基于区间验证的隐私保护真值发现算法IVPPTD (Interval Verification based Privacy-Preserving Truth Discovery).首先,采用Paillier同态加密方法实现用户感知数据的安全上传和真值发现,保护用户的感知数据、权重信息以及估算真值的隐私不被泄露.其次,提出一种密文域中的异常数据过滤算法,对数据约束区间外的异常值进行数据清洗,从而在保护用户敏感信息不被泄露的前提下,提高真值发现结果的可靠性.最后,基于感知平台和密钥生成中心协作完成真值发现过程,减少了用户与云服务器之间的通信开销.仿真实验结果表明,所提方法具有高准确率、对异常值的鲁棒性以及较低的计算开销.展开更多
基金supported by the National Natural Science Foundation of China(61472192)the Scientific and Technological Support Project(Society)of Jiangsu Province(BE2016776)
文摘The Internet now is a large-scale platform with big data. Finding truth from a huge dataset has attracted extensive attention, which can maintain the quality of data collected by users and provide users with accurate and efficient data. However, current truth finder algorithms are unsatisfying, because of their low accuracy and complication. This paper proposes a truth finder algorithm based on entity attributes (TFAEA). Based on the iterative computation of source reliability and fact accuracy, TFAEA considers the interactive degree among facts and the degree of dependence among sources, to simplify the typical truth finder algorithms. In order to improve the accuracy of them, TFAEA combines the one-way text similarity and the factual conflict to calculate the mutual support degree among facts. Furthermore, TFAEA utilizes the symmetric saturation of data sources to calculate the degree of dependence among sources. The experimental results show that TFAEA is not only more stable, but also more accurate than the typical truth finder algorithms.
文摘在群智感知系统中,用户通过提供感知数据完成感知任务.然而,由于传感设备精度、用户行为以及环境条件等因素的影响,不同用户提供的数据质量存在显著差异.真值发现技术能够有效地消除低质量数据影响,从而能够更好地利用感知数据,但现有的真值发现方法往往忽略用户个性化隐私要求,且基于加密的隐私保护技术难以应用于大量用户参与的群智感知系统.基于此,提出一种个性化差分隐私真值发现(personalized differential privacy truth discovery,PDPTD)方法.PDPTD将本地差分隐私随机响应机制应用于隐私保护,使用户能够根据个人隐私需求自主调整感知数据的扰动幅度,平衡数据隐私与可用性.服务器在聚合数据时充分考虑扰动影响,并通过加权机制提升数据质量.与此同时,PDPTD依据数据质量动态分配用户权重,即使部分用户选择较高程度的扰动,系统仍能推断出接近真实值的结果,从而保证数据的可靠性.理论分析与实验结果表明,PDPTD方案符合本地差分隐私原则,同时确保最终推断结果具有较高的精确度.
文摘隐私保护真值发现技术在移动群智感知网络领域中受到了广泛关注.然而在实际应用中,恶意用户上传的异常值对真值发现结果的可靠性带来了较大影响.为此,提出了一种基于区间验证的隐私保护真值发现算法IVPPTD (Interval Verification based Privacy-Preserving Truth Discovery).首先,采用Paillier同态加密方法实现用户感知数据的安全上传和真值发现,保护用户的感知数据、权重信息以及估算真值的隐私不被泄露.其次,提出一种密文域中的异常数据过滤算法,对数据约束区间外的异常值进行数据清洗,从而在保护用户敏感信息不被泄露的前提下,提高真值发现结果的可靠性.最后,基于感知平台和密钥生成中心协作完成真值发现过程,减少了用户与云服务器之间的通信开销.仿真实验结果表明,所提方法具有高准确率、对异常值的鲁棒性以及较低的计算开销.
