Reduce Privacy Risks

Statistical privacy, or the problem of disclosing aggregate statistics about data collected from individuals while ensuring the privacy of individual level sensitive properties, is an important problem in today's age of big data. The key challenge in statistical privacy is that applications for data collection and analysis operate on varied kinds of data, and have diverse requirements for the information that must be kept secret, and the adversaries that they must tolerate.

This project investigates how privacy can be used to inform the design and management of future data sensing systems. Networked systems that collect data about individuals will play an increasingly important role in our lives, with applications including industrial monitoring and control, "smart" homes/cities, and personalized health care. These systems will gather private information about individuals, which creates many coupled engineering challenges.

The more people use the Internet, the more they risk sharing information they don't want other people to know. Tor is a technology that every day helps millions of people protect their privacy online. Tor users -- ranging from ordinary citizens to companies with valuable intellectual property -- gain protection for the content of their online messages and activities, as well as whom they interact with and when. For the most part, Tor is very secure. However, it has a known vulnerability to an attack called website fingerprinting.

Whether it is on their smartphones, in their browsers or on social networks, people are confronted with an increasingly unmanageable number of privacy settings. What is needed is a new, more scalable paradigm that empowers them to regain control over the collection and use of their data. This is particularly the case for mobile apps people download on their smartphones. These apps have been shown to collect and share a wide variety of sensitive data, with users unable to keep up.

The ability to generate random numbers -- to flip coins -- is crucial for many computing tasks, from Monte Carlo simulation to secure communications. The theory of building such subsystems to generate random numbers is well understood, but the gap between theory and practice is surprisingly wide. As built today, these subsystems are opaque and fragile. Flaws in these subsystems can compromise the security of millions of Internet hosts.