An efficient probabilistic public key encryption scheme which hides all partial information
Proceedings of CRYPTO 84 on Advances in cryptology
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Secret sharing homomorphisms: keeping shares of a secret secret
Proceedings on Advances in cryptology---CRYPTO '86
Introduction to algorithms
A new public key cryptosystem based on higher residues
CCS '98 Proceedings of the 5th ACM conference on Computer and communications security
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Privacy Preserving Data Mining (Advances in Information Security)
Privacy Preserving Data Mining (Advances in Information Security)
ACM SIGKDD Explorations Newsletter
The case for anomalous link discovery
ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Efficient Privacy-Preserving Link Discovery
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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Link discovery is a process of identifying association(s) among different entities included in a complex network structure. These association(s) may represent any interaction among entities, for example between people or even bank accounts. The need for link discovery arises in many applications including law enforcement, counter-terrorism, social network analysis, intrusion detection, and fraud detection. Given the sensitive nature of information that can be revealed from link discovery, privacy is a major concern from the perspective of both individuals and organizations. For example in the context of financial fraud detection, linking transaction may reveal sensitive information about other individuals not involved in any fraud. In this paper, we propose an approach for link discovery in a privacy-preserving manner. We show how the problem can be reduced to finding the transitive closure of a graph. A secure split-matrix multiplication protocol based on secure scalar product computations is proposed to find the transitive closure. We analyze the performance and usability of the proposed approach.