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

Abstract

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.

Blockchain structures to guarantee logging integrity of a digital platform to support community-dwelling older adults

“…A solution recently proposed with impact to logging solutions, is the cryptographically secure chaining of information blocks, commonly known as blockchain [15]. It is famous through many digital currencies, and has been the object of significant research in different application domains [16][17][18][19][20][21][22][23][24]. Each block contains information that has some relation, and frequently also includes several common attributes: the data itself, a time stamp, a hash or cryptographic Nonce (i.e., a unique identifier or a digital digest), and the previous block's hash.…”

Secure reputation and morphism‐based offloading scheme: A veritable tool for multi‐party computation in Industrial Internet of Things

“…Therefore, any offloading scheme for IIoT-based systems must ensure self-verification of the computation results, confidentiality, integrity, fairness, data access controllability, result's completeness, and cooperation among the IIoT network. 6,7 To achieve these requirements, we develop reputation and morphism-based offloading scheme capable of ensuring self-verification results, confidentiality, integrity, fairness, data access controllability, result's completeness, and cooperation during MPC in IIoT.…”

Discovering dynamic adverse behavior of policyholders in the life insurance industry

“…Advanced data analytics approaches have attracted immense attention from the research community, business decision makers and companies to improve the gain in net profit and have shown considerably better performance via predictive and analytic capabilities (Viswanathan et al, 2020;Boodhun and Jayabalan, 2018;Olakanmi and Dada, 2019;Kaushik and Gandhi, 2019;Hutagaol and Mauritsius, 2020). In the insurance industry, while the existing methods explore the hidden behavior of dishonest policyholders, there is still potential to more accurately discover their hidden behaviors.…”