A Linear Distinguisher and its Application for Analyzing Privacy-Preserving Transformation Used in Verifiable (Outsourced) Computation

An Efficient Privacy-preserving Approach for Secure Verifiable Outsourced Computing on Untrusted Platforms

International Journal of Cloud Applications and Computing ◽

10.4018/ijcac.2019040105 ◽

2019 ◽

Vol 9 (2) ◽

pp. 79-98 ◽

Cited By ~ 10

Author(s):

Oladayo Olufemi Olakanmi ◽

Adedamola Dada

Keyword(s):

Data Storage ◽

Privacy Preservation ◽

Privacy Preserving ◽

Computational Results ◽

Outsourced Computation ◽

Proof Of Correctness

In outsourcing computation models, weak devices (clients) increasingly rely on remote servers (workers) for data storage and computations. However, most of these servers are hackable or untrustworthy, which makes their computation questionable. Therefore, there is need for clients to validate the correctness of the results of their outsourced computations and ensure that servers learn nothing about their clients other than the outputs of their computation. In this work, an efficient privacy preservation validation approach is developed which allows clients to store and outsource their computations to servers in a semi-honest model such that servers' computational results could be validated by clients without re-computing the computation. This article employs a morphism approach for the client to efficiently perform the proof of correctness of its outsourced computation without re-computing the whole computation. A traceable pseudonym is employed by clients to enforce anonymity.

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An Efficient Privacy-preserving Approach for Secure Verifiable Outsourced Computing on Untrusted Platforms

Research Anthology on Privatizing and Securing Data ◽

10.4018/978-1-7998-8954-0.ch061 ◽

2021 ◽

pp. 1299-1320

Author(s):

Oladayo Olufemi Olakanmi ◽

Adedamola Dada

Keyword(s):

Data Storage ◽

Privacy Preservation ◽

Privacy Preserving ◽

Computational Results ◽

Outsourced Computation ◽

Proof Of Correctness

In outsourcing computation models, weak devices (clients) increasingly rely on remote servers (workers) for data storage and computations. However, most of these servers are hackable or untrustworthy, which makes their computation questionable. Therefore, there is need for clients to validate the correctness of the results of their outsourced computations and ensure that servers learn nothing about their clients other than the outputs of their computation. In this work, an efficient privacy preservation validation approach is developed which allows clients to store and outsource their computations to servers in a semi-honest model such that servers' computational results could be validated by clients without re-computing the computation. This article employs a morphism approach for the client to efficiently perform the proof of correctness of its outsourced computation without re-computing the whole computation. A traceable pseudonym is employed by clients to enforce anonymity.

Download Full-text