Multiparty Computation from Threshold Homomorphic Encryption
EUROCRYPT '01 Proceedings of the International Conference on the Theory and Application of Cryptographic Techniques: Advances in Cryptology
Sharing Decryption in the Context of Voting or Lotteries
FC '00 Proceedings of the 4th International Conference on Financial Cryptography
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Secure two-party k-means clustering
Proceedings of the 14th ACM conference on Computer and communications security
Rethinking rank swapping to decrease disclosure risk
Data & Knowledge Engineering
New Efficient Attacks on Statistical Disclosure Control Mechanisms
CRYPTO 2008 Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology
Data Access in a Cyber World: Making Use of Cyberinfrastructure
Transactions on Data Privacy
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Efficient binary conversion for paillier encrypted values
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Information fusion in data privacy: A survey
Information Fusion
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Statistical disclosure control (SDC) methods aim to protect privacy of the confidential information included in some databases, for example by perturbing the non-confidential parts of the original databases. Such methods are commonly used by statistical agencies before publishing the perturbed data, which must ensure privacy at the same time as it preserves as much as possible the statistical information of the original data. In this paper we consider the problem of designing distributed privacy-preserving versions of these perturbation methods: each part of the original database is owned by a different entity, and they want to jointly compute the perturbed version of the global database, without leaking any sensitive information on their individual parts of the original data. We show that some perturbation methods do not allow a private distributed extension, whereas other methods do. Among the methods that allow a distributed privacy-preserving version, we can list noise addition, resampling and a new protection method, rank shuffling, which is described and analyzed here for the first time.