Communications of the ACM
Answering queries using views: A survey
The VLDB Journal — The International Journal on Very Large Data Bases
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Convex Optimization
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Views and queries: Determinacy and rewriting
ACM Transactions on Database Systems (TODS)
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Communications of the ACM
A firm foundation for private data analysis
Communications of the ACM
Proceedings of the 12th ACM conference on Electronic commerce
For sale : your data: by : you
Proceedings of the 10th ACM Workshop on Hot Topics in Networks
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Approximately optimal auctions for selling privacy when costs are correlated with data
Proceedings of the 13th ACM Conference on Electronic Commerce
Privacy-aware mechanism design
Proceedings of the 13th ACM Conference on Electronic Commerce
Conducting truthful surveys, cheaply
Proceedings of the 13th ACM Conference on Electronic Commerce
Secure electronic markets for private information
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Buying private data at auction: the sensitive surveyor's problem
ACM SIGecom Exchanges
Information preservation in statistical privacy and bayesian estimation of unattributed histograms
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Privacy and coordination: computing on databases with endogenous participation
Proceedings of the fourteenth ACM conference on Electronic commerce
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Personal data has value to both its owner and to institutions who would like to analyze it. Privacy mechanisms protect the owner's data while releasing to analysts noisy versions of aggregate query results. But such strict protections of individual's data have not yet found wide use in practice. Instead, Internet companies, for example, commonly provide free services in return for valuable sensitive information from users, which they exploit and sometimes sell to third parties. As the awareness of the value of the personal data increases, so has the drive to compensate the end user for her private information. The idea of monetizing private data can improve over the narrower view of hiding private data, since it empowers individuals to control their data through financial means. In this paper we propose a theoretical framework for assigning prices to noisy query answers, as a function of their accuracy, and for dividing the price amongst data owners who deserve compensation for their loss of privacy. Our framework adopts and extends key principles from both differential privacy and query pricing in data markets. We identify essential properties of the price function and micro-payments, and characterize valid solutions.