TY - GEN
T1 - Unlocking the value of privacy
T2 - 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
AU - Niu, Chaoyue
AU - Zheng, Zhenzhe
AU - Wu, Fan
AU - Tang, Shaojie
AU - Gao, Xiaofeng
AU - Chen, Guihai
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/7/19
Y1 - 2018/7/19
N2 - 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.
AB - 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.
KW - Privacy
KW - Privacy Economics of security
KW - • Security
UR - https://www.scopus.com/pages/publications/85051458660
U2 - 10.1145/3219819.3220013
DO - 10.1145/3219819.3220013
M3 - Conference contribution
AN - SCOPUS:85051458660
SN - 9781450355520
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2031
EP - 2040
BT - KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 19 August 2018 through 23 August 2018
ER -