Privacy Assurances in Multiple Data-Aggregation Transactions

In this paper, we propose a privacy-preserving algorithm for aggregating data in multiple transactions from a large number of users at a third-party application. The aggregation is performed using the most commonly used weighted sum function. The new algorithm has several novel features. First, we propose a method to generate a privacy-assurance certificate that can be easily verified by all users without significant computation effort. In particular, the computational complexity of verification does not grow with the number of users. Second, the proposed approach has a very desirable feature that users do not have to directly communicate with each other. Instead, they only communicate with the application. These features distinguish our approach from the existing research in literature.

[1]  Frederik Vercauteren,et al.  Practical Realisation and Elimination of an ECC-Related Software Bug Attack , 2012, CT-RSA.

[2]  Rajeev Motwani,et al.  Models and algorithms for data privacy , 2006 .

[3]  Craig Gentry,et al.  A fully homomorphic encryption scheme , 2009 .

[4]  Rui Zhang,et al.  PriSense: Privacy-Preserving Data Aggregation in People-Centric Urban Sensing Systems , 2010, 2010 Proceedings IEEE INFOCOM.

[5]  Pascal Paillier,et al.  Public-Key Cryptosystems Based on Composite Degree Residuosity Classes , 1999, EUROCRYPT.

[6]  Elisabeth Oswald,et al.  A Comprehensive Evaluation of Mutual Information Analysis Using a Fair Evaluation Framework , 2011, CRYPTO.

[7]  Moni Naor,et al.  Privacy preserving auctions and mechanism design , 1999, EC '99.

[8]  Xue Liu,et al.  PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[9]  Francis Y. L. Chin,et al.  Security problems on inference control for SUM, MAX, and MIN queries , 1986, JACM.

[10]  Vinod Vaikuntanathan,et al.  On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption , 2012, STOC '12.

[11]  Ivan Damgård,et al.  Secure Multiparty Computation Goes Live , 2009, Financial Cryptography.

[12]  Jacques Stern,et al.  Advances in Cryptology — EUROCRYPT ’99 , 1999, Lecture Notes in Computer Science.

[13]  Yehuda Lindell,et al.  Secure Computation on the Web: Computing without Simultaneous Interaction , 2011, IACR Cryptol. ePrint Arch..

[14]  Wenbo He,et al.  KIPDA: k-indistinguishable privacy-preserving data aggregation in wireless sensor networks , 2011, 2011 Proceedings IEEE INFOCOM.

[15]  Jonathan Katz,et al.  Secure Multi-Party Computation of Boolean Circuits with Applications to Privacy in On-Line Marketplaces , 2012, CT-RSA.

[16]  Craig Gentry,et al.  Fully homomorphic encryption using ideal lattices , 2009, STOC '09.

[17]  Andrew Chi-Chih Yao,et al.  How to generate and exchange secrets , 1986, 27th Annual Symposium on Foundations of Computer Science (sfcs 1986).

[18]  Benny Pinkas,et al.  FairplayMP: a system for secure multi-party computation , 2008, CCS.

[19]  Durgesh Kumar Mishra,et al.  Privacy Preserving k Secure Sum Protocol , 2009, ArXiv.

[20]  Joseph Bonneau,et al.  What's in a Name? , 2020, Financial Cryptography.