Privacy-preserving data mining in presence of covert adversaries

Authors:
Atsuko Miyaji;Mohammad Shahriar Rahman
Affiliations:
School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan;School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

Citing 22
Cited 2

Privacy Preserving Data Mining

CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Multiparty Computation from Threshold Homomorphic Encryption

EUROCRYPT '01 Proceedings of the International Conference on the Theory and Application of Cryptographic Techniques: Advances in Cryptology
Privacy preserving association rule mining in vertically partitioned data

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privately computing a distributed k-nn classifier

PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Privacy-preserving clustering with distributed EM mixture modeling

Knowledge and Information Systems
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Privacy-Preserving Computation of Bayesian Networks on Vertically Partitioned Data

IEEE Transactions on Knowledge and Data Engineering
Secure two-party k-means clustering

Proceedings of the 14th ACM conference on Computer and communications security
How to generate and exchange secrets

SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
An Efficient Protocol for Secure Two-Party Computation in the Presence of Malicious Adversaries

EUROCRYPT '07 Proceedings of the 26th annual international conference on Advances in Cryptology
Efficient Two-Party Secure Computation on Committed Inputs

EUROCRYPT '07 Proceedings of the 26th annual international conference on Advances in Cryptology
Non-interactive Proofs for Integer Multiplication

EUROCRYPT '07 Proceedings of the 26th annual international conference on Advances in Cryptology
Homomorphic Encryption and Signatures from Vector Decomposition

Pairing '08 Proceedings of the 2nd international conference on Pairing-Based Cryptography
Privacy-preserving data mining in the malicious model

International Journal of Information and Computer Security
Public-key cryptosystems based on composite degree residuosity classes

EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Security against covert adversaries: efficient protocols for realistic adversaries

TCC'07 Proceedings of the 4th conference on Theory of cryptography
Efficient two party and multi party computation against covert adversaries

EUROCRYPT'08 Proceedings of the theory and applications of cryptographic techniques 27th annual international conference on Advances in cryptology
Public-key encryption with non-interactive opening

CT-RSA'08 Proceedings of the 2008 The Cryptopgraphers' Track at the RSA conference on Topics in cryptology
Evaluating 2-DNF formulas on ciphertexts

TCC'05 Proceedings of the Second international conference on Theory of Cryptography
Efficient set operations in the presence of malicious adversaries

PKC'10 Proceedings of the 13th international conference on Practice and Theory in Public Key Cryptography
Public-key encryption with non-interactive opening: new constructions and stronger definitions

AFRICACRYPT'10 Proceedings of the Third international conference on Cryptology in Africa
Efficient CCA-Secure PKE from identity-based techniques

CT-RSA'10 Proceedings of the 2010 international conference on Topics in Cryptology

Privacy-preserving data mining: a game-theoretic approach

DBSec'11 Proceedings of the 25th annual IFIP WG 11.3 conference on Data and applications security and privacy
Verifiable private equality test: enabling unbiased 2-party reconciliation on ordered sets in the malicious model

Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security

Quantified Score

Hi-index	0.00

Visualization

Abstract

Disclosure of the original data sets is not acceptable due to privacy concerns in many distributed data mining settings. To address such concerns, privacy-preserving data mining has been an active research area in recent years. All the recent works on privacy-preserving data mining have considered either semi-honest or malicious adversarial models, whereby an adversary is assumed to follow or arbitrarily deviate from the protocol, respectively. While semi-honest model provides weak security requiring small amount of computation and malicious model provides strong security requiring expensive computations like Non-Interactive Zero Knowledge proofs, we envisage the need for 'covert' adversarial model that performs in between the semi-honest and malicious models, both in terms of security guarantee and computational cost. In this paper, for the first time in data-mining area, we build efficient and secure dot product and set-intersection protocols in covert adversarial model. We use homomorphic property of Paillier encryption scheme and two-party computation of Aumann et al. to construct our protocols. Furthermore, our protocols are secure in Universal Composability framework.