Verifiable private multi-party computation: Ranging and ranking

The existing work on distributed secure multi-party computation, e.g., set operations, dot product, ranking, focus on the privacy protection aspects, while the verifiability of user inputs and outcomes are neglected. Most of the existing works assume that the involved parties will follow the protocol honestly. In practice, a malicious adversary can easily forge his/her input values to achieve incorrect outcomes or simply lie about the computation results to cheat other parities. In this work, we focus on the problem of verifiable privacy preserving multiparty computation. We thoroughly analyze the attacks on existing privacy preserving multi-party computation approaches and design a series of protocols for dot product, ranging and ranking, which are proved to be privacy preserving and verifiable. We implement our protocols on laptops and mobile phones. The results show that our verifiable private computation protocols are efficient both in computation and communication.

[1]  Baruch Awerbuch,et al.  Verifiable secret sharing and achieving simultaneity in the presence of faults , 1985, 26th Annual Symposium on Foundations of Computer Science (sfcs 1985).

[2]  Benny Pinkas,et al.  Efficient Private Matching and Set Intersection , 2004, EUROCRYPT.

[3]  Tal Rabin,et al.  Verifiable secret sharing and multiparty protocols with honest majority , 1989, STOC '89.

[4]  Shaojie Tang,et al.  Privacy-preserving data aggregation without secure channel: Multivariate polynomial evaluation , 2013, 2013 Proceedings IEEE INFOCOM.

[5]  Yin Zhang,et al.  Secure friend discovery in mobile social networks , 2011, 2011 Proceedings IEEE INFOCOM.

[6]  Yehuda Lindell,et al.  Efficient Protocols for Set Intersection and Pattern Matching with Security Against Malicious and Covert Adversaries , 2008, Journal of Cryptology.

[7]  Andrew Chi-Chih Yao,et al.  Protocols for secure computations , 1982, FOCS 1982.

[8]  Taeho Jung,et al.  Search me if you can: Privacy-preserving location query service , 2012, 2013 Proceedings IEEE INFOCOM.

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

[10]  Xiang-Yang Li,et al.  Complexity of Data Collection, Aggregation, and Selection for Wireless Sensor Networks , 2011, IEEE Transactions on Computers.

[11]  Huaxiong Wang,et al.  Distributed Private Matching and Set Operations , 2008, ISPEC.

[12]  Ed Dawson,et al.  Robust, Privacy Protecting and Publicly Verifiable Sealed-Bid Auction , 2002, ICICS.

[13]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[14]  Cong Wang,et al.  Achieving usable and privacy-assured similarity search over outsourced cloud data , 2012, 2012 Proceedings IEEE INFOCOM.

[15]  Craig Gentry,et al.  Non-interactive Verifiable Computing: Outsourcing Computation to Untrusted Workers , 2010, CRYPTO.

[16]  Mikhail J. Atallah,et al.  Efficient Privacy-Preserving k-Nearest Neighbor Search , 2008, 2008 The 28th International Conference on Distributed Computing Systems.

[17]  Ming Li,et al.  FindU: Privacy-preserving personal profile matching in mobile social networks , 2011, 2011 Proceedings IEEE INFOCOM.

[18]  Bo Sheng,et al.  Verifiable Privacy-Preserving Range Query in Two-Tiered Sensor Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[19]  Xiang-Yang Li,et al.  Privacy preserving cloud data access with multi-authorities , 2012, 2013 Proceedings IEEE INFOCOM.

[20]  Arijit Ukil.  Privacy Preserving Data Aggregation in Wireless Sensor Networks , 2010, 2010 6th International Conference on Wireless and Mobile Communications.