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Relevant bibliographies by topics / Privacy preserving clustering

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Journal articles

Academic literature on the topic 'Privacy preserving clustering'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Privacy preserving clustering"

1

Hegde, Aditya, Helen Möllering, Thomas Schneider, and Hossein Yalame. "SoK: Efficient Privacy-preserving Clustering." Proceedings on Privacy Enhancing Technologies 2021, no. 4 (2021): 225–48. http://dx.doi.org/10.2478/popets-2021-0068.

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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.

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2

Lyu, Lingjuan, James C. Bezdek, Yee Wei Law, Xuanli He, and Marimuthu Palaniswami. "Privacy-preserving collaborative fuzzy clustering." Data & Knowledge Engineering 116 (July 2018): 21–41. http://dx.doi.org/10.1016/j.datak.2018.05.002.

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3

Gao, Zhiqiang, Yixiao Sun, Xiaolong Cui, Yutao Wang, Yanyu Duan, and Xu An Wang. "Privacy-Preserving Hybrid K-Means." International Journal of Data Warehousing and Mining 14, no. 2 (2018): 1–17. http://dx.doi.org/10.4018/ijdwm.2018040101.

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This article describes how the most widely used clustering, k-means, is prone to fall into a local optimum. Notably, traditional clustering approaches are directly performed on private data and fail to cope with malicious attacks in massive data mining tasks against attackers' arbitrary background knowledge. It would result in violation of individuals' privacy, as well as leaks through system resources and clustering outputs. To address these issues, the authors propose an efficient privacy-preserving hybrid k-means under Spark. In the first stage, particle swarm optimization is executed in resilient distributed datasets to initiate the selection of clustering centroids in the k-means on Spark. In the second stage, k-means is executed on the condition that a privacy budget is set as ε/2t with Laplace noise added in each round of iterations. Extensive experimentation on public UCI data sets show that on the premise of guaranteeing utility of privacy data and scalability, their approach outperforms the state-of-the-art varieties of k-means by utilizing swarm intelligence and rigorous paradigms of differential privacy.

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4

Mohassel, Payman, Mike Rosulek, and Ni Trieu. "Practical Privacy-Preserving K-means Clustering." Proceedings on Privacy Enhancing Technologies 2020, no. 4 (2020): 414–33. http://dx.doi.org/10.2478/popets-2020-0080.

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AbstractClustering is a common technique for data analysis, which aims to partition data into similar groups. When the data comes from different sources, it is highly desirable to maintain the privacy of each database. In this work, we study a popular clustering algorithm (K-means) and adapt it to the privacypreserving context.Specifically, to construct our privacy-preserving clustering algorithm, we first propose an efficient batched Euclidean squared distance computation protocol in the amortizing setting, when one needs to compute the distance from the same point to other points. Furthermore, we construct a customized garbled circuit for computing the minimum value among shared values.We believe these new constructions may be of independent interest. We implement and evaluate our protocols to demonstrate their practicality and show that they are able to train datasets that are much larger and faster than in the previous work. The numerical results also show that the proposed protocol achieve almost the same accuracy compared to a K-means plain-text clustering algorithm.

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5

Ni, Weiwei, and Zhihong Chong. "Clustering-oriented privacy-preserving data publishing." Knowledge-Based Systems 35 (November 2012): 264–70. http://dx.doi.org/10.1016/j.knosys.2012.05.012.

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6

Jahan, Thanveer. "Privacy Preserving Clustering on Distorted data." IOSR Journal of Computer Engineering 5, no. 2 (2012): 25–29. http://dx.doi.org/10.9790/0661-0522529.

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7

Wei, Weiming, Chunming Tang, and Yucheng Chen. "Efficient Privacy-Preserving K-Means Clustering from Secret-Sharing-Based Secure Three-Party Computation." Entropy 24, no. 8 (2022): 1145. http://dx.doi.org/10.3390/e24081145.

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Privacy-preserving machine learning has become an important study at present due to privacy policies. However, the efficiency gap between the plain-text algorithm and its privacy-preserving version still exists. In this paper, we focus on designing a novel secret-sharing-based K-means clustering algorithm. Particularly, we present an efficient privacy-preserving K-means clustering algorithm based on replicated secret sharing with honest-majority in the semi-honest model. More concretely, the clustering task is outsourced to three semi-honest computing servers. Theoretically, the proposed privacy-preserving scheme can be proven with full data privacy. Furthermore, the experimental results demonstrate that our proposed privacy version reaches the same accuracy as the plain-text one. Compared to the existing privacy-preserving scheme, our proposed protocol can achieve about 16.5×–25.2× faster computation and 63.8×–68.0× lower communication. Consequently, the proposed privacy-preserving scheme is suitable for secret-sharing-based secure outsourced computation.

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8

Oliveira, Stanley R. M., and Osmar R. Zaïane. "Privacy-Preserving Clustering to Uphold Business Collaboration." International Journal of Information Security and Privacy 1, no. 2 (2007): 13–36. http://dx.doi.org/10.4018/jisp.2007040102.

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9

Nguyen, Huu Hiep. "Privacy-preserving mechanisms for k-modes clustering." Computers & Security 78 (September 2018): 60–75. http://dx.doi.org/10.1016/j.cose.2018.06.003.

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10

İnan, Ali, Selim V. Kaya, Yücel Saygın, Erkay Savaş, Ayça A. Hintoğlu, and Albert Levi. "Privacy preserving clustering on horizontally partitioned data." Data & Knowledge Engineering 63, no. 3 (2007): 646–66. http://dx.doi.org/10.1016/j.datak.2007.03.015.

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