Unlocking The Value Of Privacy: Trading Aggregate Statistics Over Private Correlated Data

Authors:
Chaoyue Niu Shanghai Jiao Tong University
Zhenzhe Zheng Shanghai Jiao Tong University
Fan Wu Shanghai Jiao Tong University
Shaojie Tang The University of Texas at Dallas
Xiaofeng Gao Shanghai Jiao Tong University
Guihai Chen Shanghai Jiao Tong University

Introduction:

The authors study noisy aggregate statistics trading from the perspective of a data broker in data markets. They propose ERATO, which enables aggrEgate statistics pRicing over privATe cOrrelated data.

Abstract:

With the commoditization of personal privacy, pricing private data has become an intriguing problem. In this paper, we study noisy aggregate statistics trading from the perspective of a data broker in data markets. We thus propose ERATO, which enables aggrEgate statistics pRicing over privATe cOrrelated data. On one hand, ERATO guarantees arbitrage freeness against cunning data consumers. On the other hand, ERATO compensates data owners for their privacy losses using both bottom-up and top-down designs. We further apply ERATO to three practical aggregate statistics, namely weighted sum, probability distribution fitting, and degree distribution, and extensively evaluate their performances on MovieLens dataset, 2009 RECS dataset, and two SNAP large social network datasets, respectively. Our analysis and evaluation results reveal that ERATO well balances utility and privacy, achieves arbitrage freeness, and compensates data owners more fairly than differential privacy based approaches.

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