Homomorphically encrypted k-means on cloud-hosted servers with low client-side load

Sakellariou, Georgios; Gounaris, Anastasios

doi:10.1007/s00607-019-00711-w

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…Xing et al [29] suggested a mutual privacy-preserving k-means clustering scheme, ensuring the confidentiality of an individual's private information while preventing the leakage of the community's characteristic data. Sakellariou and Gounaris [30] presented a secure scheme with a distinctive feature of low client-side load for k-means. In this scheme, secure distance comparison operations are carried out by the trusted server.…”

Section: A Related Workmentioning

confidence: 99%

Cloud-Assisted Privacy-Preserving Spectral Clustering Algorithm Within a Multi-User Setting

Xu,

Cheng,

Tian

et al. 2024

IEEE Access

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Spectral clustering, a powerful algorithm in the field of AI, holds a significant role despite its inherent high time complexity. For data owners grappling with limitations such as small datasets and restricted computational resources, harnessing the computational capabilities of cloud computing and aggregating data from multiple sources can yield precise spectral clustering results. However, explicit data uploading to cloud servers poses privacy risks. In response to this challenge, we explore the outsourcing dilemma of spectral clustering in a cloud and multi-user environment and propose a quantum-secure and efficient solution. Specifically, by employing the CKKS homomorphic encryption algorithm within a dual non-collusive server model, we formulate a comprehensive and multi-user spectral clustering outsourcing scheme. Our approach addresses privacy concerns by introducing secure computation protocols for L 2 norm, exponential function, and negative half power function. We elaborate on efficient computational algorithms for each stage of spectral clustering, ensuring accurate clustering outcomes without compromising dataset privacy. Moreover, in our scheme, users only need to upload their encrypted dataset without requiring direct interaction with each other or the cloud server until obtaining clustering results. Finally, we argue the IND-CPA security of our design and substantiate its accuracy and efficiency through theoretical comparison analysis and experimental evaluations.

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Section: A Related Workmentioning

confidence: 99%

Cloud-Assisted Privacy-Preserving Spectral Clustering Algorithm Within a Multi-User Setting

Xu,

Cheng,

Tian

et al. 2024

IEEE Access

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“…The authors [46] also propose a solution that focuses on k-means. In this solution, the BGV scheme [47] is used.…”

Section: Individual Clusteringmentioning

confidence: 99%

Application of Homomorphic Encryption in Machine Learning

Ameur

Bouzefrane

Audigier

2022

Emerging Trends in Cybersecurity Applications

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Big data technologies, such as machine learning, have increased data utility exponentially. At the same time, the cloud has made the deployment of these technologies more accessible. However, computations of unencrypted sensitive data in a cloud environment may expose threats and Cybersecurity attacks. We consider a class of innovative cryptographic techniques called Privacy-Preserving Technologies (PPTs) to address this problem. That might help increase utility by taking more significant advantage of the cloud and machine learning technologies while preserving privacy.The first section provides a brief introduction to the so-called Homomorphic Encryption "HE" by giving an overview of the most promising schemes and then giving the current state of the art of HE tools such as Libraries and Compilers. This section aims to help non-cryptographer developers propose HE solutions by explaining what makes developing HE applications challenging.Then, we address the Privacy-Preserving in Machine Learning (PPML), an approach that allows to train and deploy ML models without exposing their private data. After exploring state-of-the-art for the most used ML models in PPML, we will overview applications of Homomorphic Encryption in Machine Learning.

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“…The computation and storage overhead of the proposed scheme is lower, compared to the previous works. The authors of [220] introduced an FHE-based k-means clustering protocol also with a low client workload. In the secure solution, to lighten the clients burden, the comparison operations (which are initially performed by the client) are completed by an additional entity-a trusted and auditable server.…”

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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Homomorphically encrypted k-means on cloud-hosted servers with low client-side load

Cited by 13 publications

References 18 publications

Cloud-Assisted Privacy-Preserving Spectral Clustering Algorithm Within a Multi-User Setting

Cloud-Assisted Privacy-Preserving Spectral Clustering Algorithm Within a Multi-User Setting

Application of Homomorphic Encryption in Machine Learning

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

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