To maintain the privacy of individual users ’ personal data, a growing number of researchers propose storing user data in client computers or personal data stores in the cloud, and allowing users to tightly control the release of that data. While this allows specific applications to use certain approved user data, it precludes broad statistical analysis of user data. Distributed differential privacy is one approach to enabling this analysis, but previous proposals are not practical in that they scale poorly, or that they require trusted clients. This paper proposes a design that overcomes these limitations. It places tight bounds on the extent to which malicious clients can distort answers, scales well, and tolerates churn among clients. This paper presents a detailed design and analysis, and gives performance results of a complete implementation based on the deployment of over 600 clients.
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