HisTorε: Differentially Private and Robust Statistics Collection for Tor

Privacy-preserving Anomaly Detection in Tor

This extended abstract presents our vision of PrivEy, a distributed data collection and anomaly detection framework for the Tor network. PrivEy builds on the general framework of PrivEx (CCS 2014), a system for privately collecting statistics about traffic egressing the Tor network; however, PrivEy extends PrivEx in several important respects: (i) it supports the collection of a wider array of ...

Efficient Private Statistics with Succinct Sketches

Large-scale collection of contextual information is often essential in order to gather statistics, train machine learning models, and extract knowledge from data. The ability to do so in a privacy-preserving way – i.e., without collecting finegrained user data – enables a number of additional computational scenarios that would be hard, or outright impossible, to realize without strong privacy g...

Preamble of Constitution VS Govt.'s Policy to Promote Private Higher Educational Institutions in India: A Mathematical Model for Solution

Convergence Rates for Differentially Private Statistical Estimation

Differential privacy is a cryptographically-motivated definition of privacy which has gained significant attention over the past few years. Differentially private solutions enforce privacy by adding random noise to a function computed over the data, and the challenge in designing such algorithms is to control the added noise in order to optimize the privacy-accuracy-sample size tradeoff. This w...

Differentially Private Local Electricity Markets

Privacy-preserving electricity markets have a key role in steering customers towards participation in local electricity markets by guarantying to protect their sensitive information. Moreover, these markets make it possible to statically release and share the market outputs for social good. This paper aims to design a market for local energy communities by implementing Differential Privacy (DP)...