Designing privacy-preserving interval operations based on homomorphic encryption and secret sharing techniques

Wüller, Stefan; Mayer, Daniel A.; Forg, Fabian; Schüppen, Samuel; Assadsolimani, Benjamin; Meyer, Ulrike; Wetzel, Susanne

doi:10.3233/jcs-16830

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…As already mentioned, the implementation of the homomorphic encryption is not yet possible because of some problems related to performance. Several researches trying to find solutions and alternatives that will remedy to these problems while providing models that ensures users privacy in clouds (Wüller et al, 2017;Chen et al, 2014). Tebaa et al (2015) propose a hybrid homomorphic system that is applied to the banking data to perform operations on encrypted data without decrypting it.…”

Section: Related Work and Research Scopementioning

confidence: 99%

A New Authentication and Homomorphic Encryption as a Service Model for Preserving Privacy in Clouds

Zkik¹,

Tebaa²,

Tachihante³

et al. 2017

Journal of Computer Science

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The security as a Service (SECaaS) is a new model which provides security solution to users through Cloud Computing. The maturity of Cloud Computing services makes possible the use of the SECaaS model. This new model offers huge benefits to users, such as Authentication as a Service (AaaS) and Encryption as a Service (ENCaaS). So, it can offer more security features, since it uses the resources of Clouds and it's connected to the different security policy databases. While SECaaS offers to cloud users and companies a multitude of security services, it still remains very limited and several aspects of security are not covered by this model, especially the part concerning the privacy. In addition, SECaaS is a new model that is not yet correctly deployed and it is not sufficiently solicited by companies. On the other side, Homomorphic encryption is considered as a good solution to ensure the privacy for users using the cloud services because it permits to make calculation on cipher text and data without decrypting them, but this solution suffer from many limitations such as the key size, the high latency and some serious performance problems. The main idea of this paper it's to propose a new security model to preserving user's privacy using homomorphic encryption while bypassing its limitations. So, This paper proposes a framework for Authentication and Homomorphic Encryption (A-HEaaS) based on security as a Service model which permits a secure access to the Cloud servers and the use of homomorphic encryption for calculations on encrypted data. The paper describes the design of our model and gives an implementation of our framework on medical Data.

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Section: Related Work and Research Scopementioning

confidence: 99%

A New Authentication and Homomorphic Encryption as a Service Model for Preserving Privacy in Clouds

Zkik¹,

Tebaa²,

Tachihante³

et al. 2017

Journal of Computer Science

View full text Add to dashboard Cite

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“… 16 17 18 However, more complex protocols applying homomorphic encryption techniques are also feasible. 19 While multiple SMPC frameworks have been developed in the past, most approaches are impeded by the connection and network setup of the participating entities due to the computational overhead induced by the chosen SMPC protocol. Especially, if the level of computational complexity increases (e.g., additional parties or more complex tasks), the network bandwidth could pose a significant bottleneck yielding inefficient runtimes.…”

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Distributed Analytics Platform for Health Care Data

et al. 2022

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Background In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of health care data. However, data protection regulations prohibit data centralisation for analysis purposes because of potential privacy risks like the accidental disclosure of data to third parties. Therefore, alternative data usage policies, which comply with present privacy guidelines, are of particular interest. Objective We aim to enable analyses on sensitive patient data by simultaneously complying with local data protection regulations using an approach called the Personal Health Train (PHT), which is a paradigm that utilises distributed analytics (DA) methods. The main principle of the PHT is that the analytical task is brought to the data provider and the data instances remain in their original location. Methods In this work, we present our implementation of the PHT paradigm, which preserves the sovereignty and autonomy of the data providers and operates with a limited number of communication channels. We further conduct a DA use case on data stored in three different and distributed data providers. Results We show that our infrastructure enables the training of data models based on distributed data sources. Conclusion Our work presents the capabilities of DA infrastructures in the health care sector, which lower the regulatory obstacles of sharing patient data. We further demonstrate its ability to fuel medical science by making distributed data sets available for scientists or health care practitioners.

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