TY - GEN
T1 - The value of privacy
T2 - 13th Joint International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS / IFIP Performance 2016
AU - Wang, Weina
AU - Ying, Lei
AU - Zhang, Junshan
N1 - Publisher Copyright:
© 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2016/6/14
Y1 - 2016/6/14
N2 - We study the value of data privacy in a game-theoretic model of trading private data, where a data collector pur- chases private data from strategic data subjects (individu-als) through an incentive mechanism. The private data of each individual represents her knowledge about an underly- ing state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy- preserving version of her data. In this paper, the value of ϵ units of privacy is measured by the minimum payment of all nonnegative payment mech- Anisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ϵ. The higher ϵ is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects be- comes large. Speciffically, the lower bound assures that it is impossible to use less amount of payment to buy ϵ units of privacy, and the upper bound is given by an achievable pay- ment mechanism that we designed. Based on these funda- mental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individ- ual's payment away from the minimum.
AB - We study the value of data privacy in a game-theoretic model of trading private data, where a data collector pur- chases private data from strategic data subjects (individu-als) through an incentive mechanism. The private data of each individual represents her knowledge about an underly- ing state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy- preserving version of her data. In this paper, the value of ϵ units of privacy is measured by the minimum payment of all nonnegative payment mech- Anisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ϵ. The higher ϵ is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects be- comes large. Speciffically, the lower bound assures that it is impossible to use less amount of payment to buy ϵ units of privacy, and the upper bound is given by an achievable pay- ment mechanism that we designed. Based on these funda- mental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individ- ual's payment away from the minimum.
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U2 - 10.1145/2896377.2901461
DO - 10.1145/2896377.2901461
M3 - Conference contribution
AN - SCOPUS:84978696568
T3 - SIGMETRICS/ Performance 2016 - Proceedings of the SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Science
SP - 249
EP - 260
BT - SIGMETRICS/ Performance 2016 - Proceedings of the SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Science
PB - Association for Computing Machinery, Inc
Y2 - 14 June 2016 through 18 June 2016
ER -