Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Communications of the ACM
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Communications of the ACM
Machine Learning
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Universally Ideal Secret Sharing Schemes (Preliminary Version)
CRYPTO '92 Proceedings of the 12th Annual International Cryptology Conference on Advances in Cryptology
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Privacy-Preserving Data Mining: Why, How, and When
IEEE Security and Privacy
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 clustering on horizontally partitioned data
Data & Knowledge Engineering
Sign Detection and Implicit-Explicit Conversion of Numbers in Residue Arithmetic
IEEE Transactions on Computers
Secure two-party k-means clustering
Proceedings of the 14th ACM conference on Computer and communications security
Oblivious neural network computing via homomorphic encryption
EURASIP Journal on Information Security
Fully homomorphic encryption using ideal lattices
Proceedings of the forty-first annual ACM symposium on Theory of computing
Blind authentication: a secure crypto-biometric verification protocol
IEEE Transactions on Information Forensics and Security
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
Chinese remaindering with errors
IEEE Transactions on Information Theory
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
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This paper introduces an efficient privacy-preserving protocol for distributed K-means clustering over an arbitrary partitioned data, shared among N parties. Clustering is one of the fundamental algorithms used in the field of data mining. Advances in data acquisition methodologies have resulted in collection and storage of vast quantities of user’s personal data. For mutual benefit, organizations tend to share their data for analytical purposes, thus raising privacy concerns for the users. Over the years, numerous attempts have been made to introduce privacy and security at the expense of massive additional communication costs. The approaches suggested in the literature make use of the cryptographic protocols such as Secure Multiparty Computation (SMC) and/or homomorphic encryption schemes like Paillier’s encryption. Methods using such schemes have proven communication overheads. And in practice are found to be slower by a factor of more than 106. In light of the practical limitations posed by privacy using the traditional approaches, we explore a paradigm shift to side-step the expensive protocols of SMC. In this work, we use the paradigm of secret sharing, which allows the data to be divided into multiple shares and processed separately at different servers. Using the paradigm of secret sharing, allows us to design a provably-secure, cloud computing based solution which has negligible communication overhead compared to SMC and is hence over a million times faster than similar SMC based protocols.