Implementing private k-means clustering using a LWE-based cryptosystem

Theodouli, Anastasia; Draziotis, Konstantinos A.; Gounaris, Anastasios

doi:10.1109/iscc.2017.8024510

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…In order to solve this problem, Zhang et al [26] introduced contour coefficients into the privacy-preserving scheme of kmeans clustering. Theodouli et al [27] put forth an encryption system framework based on the Learning With error (LWE) problem to evaluate the k-means algorithm. Kim and Chang [28] constructed an efficient secure comparison protocol for privacy-preserving k-means clustering scheme.…”

Section: A Related Workmentioning

confidence: 99%

“…Overall, the primary technologies employed for designing privacy-preserving clustering schemes can be broadly categorized into two types: differential privacy ( [22], [25], [26], [35]- [38], [41]) and homomorphic encryption ( [21], [23], [24], [27]- [32], [34], [39], [40]). The introduction of differential privacy often impacts the accuracy of the model because it protects individual data privacy by introducing random noise, potentially affecting the computation and reducing result accuracy.…”

Section: A Related Workmentioning

confidence: 99%

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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%

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 [42] demonstrate a solution to perform k-means using a collaboration between the client and a server. They used the BV scheme [43].…”

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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“…Based on an LWE-based homomorphic cryptosystem, Theodouli et al [218] introduced a privacy-preserving k-means clustering framework, and also described three user-server interactive algorithms for computing new clusters. Receiving the encrypted distances (between data records and current clusters), the client decrypts the distances and compares the distances to obtain the individual minimum for each data record.…”

Section: ) Clustering and Association Rule Miningmentioning

confidence: 99%

“…The three algorithms achieve different tradeoffs between the security and the consumed resources of the client and the server. In practical applications, the above schemes [216]- [218] still require nonnegligible computation burdens for the users, whether for computing the trapdoor information or decrypting all distance values. To reduce the participation amount of the users, Almutairi et al [219] proposed a homomorphic k-means solution by employing a structure called updatable distance matrix (UDM) for storing the information of data records.…”

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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Implementing private k-means clustering using a LWE-based cryptosystem

Cited by 7 publications

References 9 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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