Practical Privacy-Preserving K-means Clustering

Mohassel, Payman; Rosulek, Mike; Trieu, Ni

doi:10.2478/popets-2020-0080

Cited by 43 publications

(51 citation statements)

References 46 publications

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“…Algorithms based on secure multiparty computation (SMC) (Goldreich, 2009) can preserve the security and privacy of users' data. There existed literatures utilizing SMC like (Lindell & Pinkas, 2008;Mohassel, Rosulek & Trieu, 2020;Upmanyu, Namboodiri, Srinathan & Jawahar, 2010), in which algorithms were proposed to perform distributed data mining without revealing private inputs of participants. In addition, Clifton et al (Clifton, Kantarcioglu, Vaidya, Lin & Zhu, 2002) proposed that a relatively small set of cryptography primitive should be utilized to generate the SMC protocol.…”

Section: Related Workmentioning

confidence: 99%

Efficient and Privacy-Preserving Multi-User Outsourced K-Means Clustering

Li¹,

Huang²,

Li³

et al. 2021

CIS

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In recent years, with the development of the Internet, the data on the network presents an outbreak trend. Big data mining aims at obtaining useful information through data processing, such as clustering, clarifying and so on. Clustering is an important branch of big data mining and it is popular because of its simplicity. A new trend for clients who lack of storage and computational resources is to outsource the data and clustering task to the public cloud platforms. However, as datasets used for clustering may contain some sensitive information (e.g., identity information, health information), simply outsourcing them to the cloud platforms can't protect the privacy. So clients tend to encrypt their databases before uploading to the cloud for clustering. In this paper, we focus on privacy protection and efficiency promotion with respect to k-means clustering, and we propose a new privacy-preserving multi-user outsourced k-means clustering algorithm which is based on locality sensitive hashing (LSH). In this algorithm, we use a Paillier cryptosystem encrypting databases, and combine LSH to prune off some unnecessary computations during the clustering. That is, we don't need to compute the Euclidean distances between each data record and each clustering center. Finally, the theoretical and experimental results show that our algorithm is more efficient than most existing privacy-preserving k-means clustering.

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Section: Related Workmentioning

confidence: 99%

Efficient and Privacy-Preserving Multi-User Outsourced K-Means Clustering

Li¹,

Huang²,

Li³

et al. 2021

CIS

View full text Add to dashboard Cite

show abstract

“…HE allows to directly compute functions on encrypted data. The second paradigm uses secure multi-party computation (MPC) [10,11]. MPC allows mutually distrusting parties to collaboratively compute a joint function over their respective private data.…”

Section: Introductionmentioning

confidence: 99%

“…Unintended common characteristics between patients might Costly computation due to use of bit-wise encryption. MPC-KMeans [11] outperforms this scheme by 5000× for 400 data records. ‡ [18] is 194× faster than this scheme for 400 data records.…”

Section: Introductionmentioning

confidence: 99%

“…− An empirical evaluation of the four most efficient and fully private clustering schemes [9,11,18,19], cf. Tab.…”

Section: Introductionmentioning

confidence: 99%

“…− An implementation of the clustering protocol of [9] and [19] in C++17. Implementations of the remaining two protocols that we also evaluate [11,18] are publicly available. Our code is available at https://encrypto.de/ code/SoK_ppClustering.…”

Section: Introductionmentioning

confidence: 99%

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SoK: Efficient Privacy-preserving Clustering

Hegde

Möllering

Schneider

et al. 2021

Proceedings on Privacy Enhancing Technologies

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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.

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