Privacy-Preserving Matching Protocols for Attributes and Strings

In this technical report we present two new privacy-preserving matching protocols for singular attributes and strings, respectively. The first one is used for matching of common attributes without revealing unmatched ones to each other. The second protocol is used to discover the longest common sub-string of two input strings in a privacy-preserving manner. Compared with previous work, our solutions are efficient and suitable to implement for many different applications, e.g., discovery of common worm signatures, computation of similarity of IP payloads

Towards Privacy Preserving of Forensic DNA Databases

Protecting privacy of individuals is critical for forensic genetics. In a kinship/identity testing, related DNA profiles between user's query and the DNA database need to be extracted. However, unrelated profiles cannot be revealed to each other. The challenge is today's DNA database usually contains millions of DNA profiles, which is too big to perform privacy-preserving query with current cryptosystem directly. In this thesis, we propose a scalable system to support privacy-preserving query in DNA Database. A two-phase strategy is designed: the first is a Short Tandem Repeat index tree for quick fetching candidate profiles from disk. It groups loci of DNA profiles by matching probability, so as to reduce I/O cost required to find a particular profile. The second is an Elliptic Curve Cryptosystem based privacy-preserving matching engine, which performs match between candidates and user's sample. In particular, a privacy-preserving DNA profile matching algorithm is designed, which achieves O(n) computing time and communication cost. Experimental results show that our system performs well at query latency, query hit rate, and communication cost. For a database of one billion profiles, it takes 80 seconds to return results to the user