Outsourcing Two-Party Privacy Preserving K-Means Clustering Protocol in Wireless Sensor Networks

Liu, Xiaoyan; Jiang, Zoe L.; Yiu, Siu-Ming; Wang, Xuan; Tan, Chuting; Li, Ye; Liu, Zechao; Jin, Yabin; Fang, Junbin

doi:10.1109/msn.2015.42

Cited by 18 publications

(8 citation statements)

References 26 publications

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“…However, this work reveals the intermediate closet clustering centers to the server. Many recent works focus on clustering in the outsourcing setting (many parties and a trusted/untrusted mediator) [30,37,48,52], or differential privacy setting [8,51,53,54,62]. There are few recent works [18,28,43,58] that consider privacy preserving K-means clustering with full privacy guarantees.…”

Section: Related Workmentioning

confidence: 99%

Practical Privacy-Preserving K-means Clustering

Mohassel

Rosulek

Trieu

2020

Proceedings on Privacy Enhancing Technologies

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Clustering 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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Section: Related Workmentioning

confidence: 99%

Practical Privacy-Preserving K-means Clustering

Mohassel

Rosulek

Trieu

2020

Proceedings on Privacy Enhancing Technologies

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“…The K-Means-Algorithm has been a subject of interest in the context of privacy-preserving computations for some time, but to our knowledge all previous works like [9], [25], [24], [31] and [42] require interaction between several parties, e.g. via Multiparty Computation (MPC).…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Machine Learning on Encrypted Data

Jäschke

Armknecht

2019

Lecture Notes in Computer Science

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In the context of Fully Homomorphic Encryption, which allows computations on encrypted data, Machine Learning has been one of the most popular applications in the recent past. All of these works, however, have focused on supervised learning, where there is a labeled training set that is used to configure the model. In this work, we take the first step into the realm of unsupervised learning, which is an important area in Machine Learning and has many real-world applications, by addressing the clustering problem. To this end, we show how to implement the K-Means-Algorithm. This algorithm poses several challenges in the FHE context, including a division, which we tackle by using a natural encoding that allows division and may be of independent interest. While this theoretically solves the problem, performance in practice is not optimal, so we then propose some changes to the clustering algorithm to make it executable under more conventional encodings. We show that our new algorithm achieves a clustering accuracy comparable to the original K-Means-Algorithm, but has less than 5% of its runtime.

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“…An extending homomorphic scheme of [216] was designed by Liu et al [217], involving two data owners holding horizontal partitions of the dataset. In each clustering round, the server allocates each encrypted data record to the nearest cluster, and the two data owners compute new cluster centers and uploads the encrypted clusters to the server for the next clustering operation.…”

Section: ) Clustering and Association Rule Miningmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

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With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field. INDEX TERMS Secure outsourced computing, privacy preserving, homomorphic encryption, secure outsourced machine learning, data processing.

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Outsourcing Two-Party Privacy Preserving K-Means Clustering Protocol in Wireless Sensor Networks

Cited by 18 publications

References 26 publications

Practical Privacy-Preserving K-means Clustering

Practical Privacy-Preserving K-means Clustering

Unsupervised Machine Learning on Encrypted Data

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

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