Achieving Secure and Differentially Private Computations in Multiparty Settings
Achieving Secure and Differentially Private Computations in Multiparty Settings
复制标题
在多方设置中实现安全和差分隐私计算
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
? ?1∗
中科院分区：
文献类型：
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作者：
Abbas Acar;Z. B. Celik;Hidayet Aksu;A. Uluagac;P. Mcdaniel;? ?????;? ??????∗;? ?;? ?∗;?;? ?????;? ?∗;?;? ??;? ??;? ? ?∗;? ?1∗
由于安全和隐私问题，在从医疗保健到金融的分布式环境中共享和处理敏感数据是一项重大挑战。安全多方计算（SMC）是解决这一问题的可行灵丹妙药，它允许分布式各方进行计算，而各方对其数据一无所知，但知道最终结果。虽然SMC在这种分布式环境中很重要，但它不能保证不向对手泄露任何个人信息。差分隐私（DP）可以用来解决这个问题;然而，用DP实现SMC也不是一个简单的任务。在本文中，我们提出了一种新的安全多方分布式差分私有（SM-DDP）协议，以实现安全和私有的计算在多方环境中。具体来说，我们的协议，我们同时实现SMC和DP在分布式设置集中在线性回归水平分布的数据。也就是说，各方不能看到彼此的数据，并且不能从最终构建的统计模型中推断出关于个人的信息。任何允许独立计算局部统计的统计模型函数都可以通过我们的协议计算。该协议实现了对SMC的同态加密和对DP的功能机制，以实现所需的安全性和隐私性保证。在这项工作中，我们首先介绍了SM-DDP协议的理论基础，然后在两个不同的数据集上评估其有效性和性能。我们的研究结果表明，一个可以实现个人级别的隐私，通过建议的协议与分布式DP，这是独立地适用于每一方在一个分布式的方式。此外，我们的研究结果还表明，SM-DDP协议产生的计算开销最小，是可扩展的，并提供安全和隐私保证。
Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (SMC) is a viable panacea for this, allowing distributed parties to make computations while the parties learn nothing about their data, but the final result. Although SMC is instrumental in such distributed settings, it does not provide any guarantees not to leak any information about individuals to adversaries. Differential privacy (DP) can be utilized to address this; however, achieving SMC with DP is not a trivial task, either. In this paper, we propose a novel Secure Multiparty Distributed Differentially Private (SM-DDP) protocol to achieve secure and private computations in a multiparty environment. Specifically, with our protocol, we simultaneously achieve SMC and DP in distributed settings focusing on linear regression on horizontally distributed data. That is, parties do not see each others’ data and further, can not infer information about individuals from the final constructed statistical model. Any statistical model function that allows independent calculation of local statistics can be computed through our protocol. The protocol implements homomorphic encryption for SMC and functional mechanism for DP to achieve the desired security and privacy guarantees. In this work, we first introduce the theoretical foundation for the SM-DDP protocol and then evaluate its efficacy and performance on two different datasets. Our results show that one can achieve individual-level privacy through the proposed protocol with distributed DP, which is independently applied by each party in a distributed fashion. Moreover, our results also show that the SM-DDP protocol incurs minimal computational overhead, is scalable, and provides security and privacy guarantees.
DOI：
10.1109/sp.2017.35
发表时间：
2017-05
期刊：
2017 IEEE Symposium on Security and Privacy (SP)
影响因子：
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作者：
Adam D. Smith;Abhradeep Thakurta;Jalaj Upadhyay
通讯作者：
Adam D. Smith;Abhradeep Thakurta;Jalaj Upadhyay
DOI：
10.1056/nejmoa0809329
发表时间：
2009-02-19
期刊：
The New England journal of medicine
影响因子：
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作者：
International Warfarin Pharmacogenetics Consortium;Klein TE;Altman RB;Eriksson N;Gage BF;Kimmel SE;Lee MT;Limdi NA;Page D;Roden DM;Wagner MJ;Caldwell MD;Johnson JA
通讯作者：
Johnson JA