We present techniques for privacy-preserving computation of multidimensional aggregates on data partitioned across multiple clients. Data from different clients is perturbed (rand...
Rakesh Agrawal, Ramakrishnan Srikant, Dilys Thomas
Differential privacy has gained a lot of attention in recent years as a general model for the protection of personal information when used and disclosed for secondary purposes. It...
In many important applications, a collection of mutually distrustful parties must perform private computation over multisets. Each party’s input to the function is his private i...
Abstract. Data aggregation is a key aspect of many distributed applications, such as distributed sensing, performance monitoring, and distributed diagnostics. In such settings, use...
Krishna P. N. Puttaswamy, Ranjita Bhagwan, Venkata...
This study aims to develop techniques for allowing users to form more accurate expectations of privacy. We have developed a peripheral display for notifying users when their compu...