SoK: Efficient Privacy-preserving Clustering

Abstract

Clustering is a popular unsupervised machine learning technique that groups similar input elements into clusters. It is used in many areas ranging from business analysis to health care. In many of these applications, sensitive information is clustered that should not be leaked. Moreover, nowadays it is often required to combine data from multiple sources to increase the quality of the analysis as well as to outsource complex computation to powerful cloud servers. This calls for efficient privacy-preserving clustering. In this work, we systematically analyze the state-of-the-art in privacy-preserving clustering. We implement and benchmark today’s four most efficient fully private clustering protocols by Cheon et al. (SAC’19), Meng et al. (ArXiv’19), Mohassel et al. (PETS’20), and Bozdemir et al. (ASIACCS’21) with respect to communication, computation, and clustering quality. We compare them, assess their limitations for a practical use in real-world applications, and conclude with open challenges.

Robustness Analysis of a Novel Model-Based Recommendation Algorithms in Privacy Environment

“…Experiments show that the proposed system is effective, scalable, and makes accurate recommendations. Hedge et al [34] conducted a comprehensive analysis of privacy-preserving clustering techniques. They implemented and evaluated four efficient clustering protocols that ensure complete privacy.…”

SoK: Secure Aggregation Based on Cryptographic Schemes for Federated Learning

“…The systematization is based on the learning algorithm, the collaborative model, the protection mechanism, and the threat model. In [HMSY21], the authors review and analyze techniques and protocols used for privacy-preserving clustering with respect to efficiency, privacy, and security models. The above studies focus on specific types of machine learning models that require special treatment to be trained collaboratively.…”

Efficient Privacy-Preserving K-Means Clustering from Secret-Sharing-Based Secure Three-Party Computation

“…As an unsupervised machine learning technique, similar input records are grouped into clusters while records belonging to different clusters should be maximally different [ 3 ]. In this work, we focus on clustering, which plays an extremely important role in data processing and analysis.…”

“…The K -means algorithm is one of the most well-known clustering algorithms. A privacy-preserving K-means clustering, which has full data privacy, allows the parties to cluster their combined datasets without revealing any other information except for the final centroid [ 3 ]. In other words, the information of intermediate centroids, cluster assignments, and cluster sizes should be protected in the protocol.…”

“…Upmanyu et al [ 25 ] and Baby and Chandra [ 26 ] respectively presented a distributed threshold secret sharing scheme based on the Chinese remainder theorem (CRT-SS). However, none of them provide full privacy guarantees as shown in [ 3 ]. In 2020, Mohassel et al [ 27 ] presented 2PC K -means clustering protocol with 2-out-of-2 additive secret sharing, and although it provides full data privacy, it is inefficient in terms of computation and communication overhead, because their work heavily relies on garbled circuit and oblivious transfer (OT).…”