Privacy-Preserving Distributed Information Sharing
Abstract
In many important applications, a collection of mutually distrustful parties must share information, without compromising their privacy. Currently, these applications are often performed by using some form of a trusted third party (TTP); this TTP receives all players inputs, computes the desired function, and returns the result. However, the level of trust that must be placed in such a TTP is often inadvisable, undesirable, or even illegal. In order to make many applications practical and secure, we must remove the TTP, replacing it with efficient protocols for privacy-preserving distributed information sharing. Thus, in this thesis we explore techniques for privacy-preserving distributed information sharing that are efficient, secure, and applicable to many situations. As an example of privacy-preserving information sharing, we propose efficient techniques for privacy-preserving operations on multisets. By building a framework of multiset operations, employing the mathematical properties of polynomials, we design efficient, secure, and composable methods to enable privacy-preserving computation of the union, intersection, and element reduction operations. Additionally, we address the problem of determining Subset relations, and even use our techniques to evaluate CNF boolean formulae. We then examine the problem of hot item identification and publication. Many applications of this problem require greater efficiency and robustness than any previously-designed secure protocols for this problem. In order to achieve sufficiently efficient protocols for these problems, we define two new privacy properties: owner privacy and data privacy. By designing our protocols to achieve owner and data privacy, we are able to significantly increase efficiency over our privacy-preserving set operations, while still protecting the privacy of participants.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2006
- Accession Number
- ADA459056
Entities
People
- Lea Kissner
Organizations
- Carnegie Mellon University