Crowds: anonymity for Web transactions
ACM Transactions on Information and System Security (TISSEC)
Untraceable electronic mail, return addresses, and digital pseudonyms
Communications of the ACM
Foundations of Cryptography: Basic Tools
Foundations of Cryptography: Basic Tools
Multiparty Computation from Threshold Homomorphic Encryption
EUROCRYPT '01 Proceedings of the International Conference on the Theory and Application of Cryptographic Techniques: Advances in Cryptology
PKC '01 Proceedings of the 4th International Workshop on Practice and Theory in Public Key Cryptography: Public Key Cryptography
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Tor: the second-generation onion router
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Privacy Preserving Nearest Neighbor Search
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
ACM Transactions on Computer Systems (TOCS)
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Efficient network aware search in collaborative tagging sites
Proceedings of the VLDB Endowment
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
De-anonymizing Social Networks
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Gossiping personalized queries
Proceedings of the 13th International Conference on Extending Database Technology
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Practical and secure solutions for integer comparison
PKC'07 Proceedings of the 10th international conference on Practice and theory in public-key cryptography
Multiparty computation for interval, equality, and comparison without bit-decomposition protocol
PKC'07 Proceedings of the 10th international conference on Practice and theory in public-key cryptography
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Distributed paillier cryptosystem without trusted dealer
WISA'10 Proceedings of the 11th international conference on Information security applications
The GOSSPLE anonymous social network
Proceedings of the ACM/IFIP/USENIX 11th International Conference on Middleware
Privacy-preserving set operations
CRYPTO'05 Proceedings of the 25th annual international conference on Advances in Cryptology
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
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In this paper, we address the problem of computing the similarity between two users (according to their profiles) while preserving their privacy in a fully decentralized system and for the passive adversary model. First, we introduce a two-party protocol for privately computing a threshold version of the similarity and apply it to well-known similarity measures such as the scalar product and the cosine similarity. The output of this protocol is only one bit of information telling whether or not two users are similar beyond a predetermined threshold. Afterwards, we explore the computation of the exact and threshold similarity within the context of differential privacy. Differential privacy is a recent notion developed within the field of private data analysis guaranteeing that an adversary that observes the output of the differentially private mechanism, will only gain a negligible advantage (up to a privacy parameter) from the presence (or absence) of a particular item in the profile of a user. This provides a strong privacy guarantee that holds independently of the auxiliary knowledge that the adversary might have. More specifically, we design several differentially private variants of the exact and threshold protocols that rely on the addition of random noise tailored to the sensitivity of the considered similarity measure. We also analyze their complexity as well as their impact on the utility of the resulting similarity measure. Finally, we provide experimental results validating the effectiveness of the proposed approach on real datasets.