Protect

The rapid advancement of techniques for secure computation on protected data offers a major incentive for development of tools for general-purpose secure computation that protects data privacy, as opposed to computation of specialized tasks. The recent emergence of cloud computing and the need to protect privacy of sensitive data used in outsourced computation serves as another major motivation for this work. With this in mind, this project targets at developing a compiler suitable for privacy-preserving execution of any functionality specified by a user program.

The Public Key Infrastructure (PKI), along with the Secure Sockets Layer (SSL) and Transport Layer Security (TLS) protocols, are responsible for securing Internet transactions such as banking, email, and e-commerce; they provide users with the ability to verify with whom they are communicating online, and enable encryption of those communications. While the use of the PKI is mostly automated, there is a surprising amount of human intervention in management tasks that are crucial to its proper operation.

This project is developing new techniques for manipulating sensitive data by exploring two related areas, computing on private keys and computing on authenticated data. Currently, a private key is an inert object that gives its holder the ability to perform a cryptographic operation on all messages, as may be the case when generating a signature. The project is exploring a new vision, in which computing on the private key itself creates new restricted private keys that can only perform restricted operations such as, for example, signing only some messages but not others.

Information about individuals is collected by a variety of organizations including government agencies, banks, hospitals, research institutions, and private companies. In many cases, sharing this data among organizations can bring benefits in social, scientific, business, and security domains, as the collected information is of similar nature, of about similar populations. However, much of this collected data is sensitive as it contains personal information, or information that could damage an organization's reputation or competitiveness.

Social networks provide many benefits, but also give rise to serious concerns regarding privacy. Indeed, since privacy protections are not intrinsically incorporated into the underlying technological framework, user data is still accessible to the social network and is open to misuse. While there have been efforts to incorporate privacy into social networks, existing solutions are not sufficiently lightweight, transparent, and functional, and therefore have achieved only limited adoption.