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Functional encryption based approaches for practical privacy-preserving machine learning

Xu, Runhua (2020) Functional encryption based approaches for practical privacy-preserving machine learning. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Machine learning (ML) is increasingly being used in a wide variety of application domains. However, deploying ML solutions poses a significant challenge because of increasing privacy concerns, and requirements imposed by privacy-related regulations. To tackle serious privacy concerns in ML-based applications, significant recent research efforts have focused on developing privacy-preserving ML (PPML) approaches by integrating into ML pipeline existing anonymization mechanisms or emerging privacy protection approaches such as differential privacy, secure computation, and other architectural frameworks. While promising, existing secure computation based approaches, however, have significant computational efficiency issues and hence, are not practical.

In this dissertation, we address several challenges related to PPML and propose practical secure computation based approaches to solve them. We consider both two-tier cloud-based and three-tier hybrid cloud-edge based PPML architectures and address both emerging deep learning models and federated learning approaches. The proposed approaches enable us to outsource data or update a locally trained model in a privacy-preserving manner by employing computation over encrypted datasets or local models. Our proposed secure computation solutions are based on functional encryption (FE) techniques. Evaluation of the proposed approaches shows that they are efficient and more practical than existing approaches, and provide strong privacy guarantees. We also address issues related to the trustworthiness of various entities within the proposed PPML infrastructures. This includes a third-party authority (TPA) which plays a critical role in the proposed FE-based PPML solutions, and cloud service providers. To ensure that such entities can be trusted, we propose a transparency and accountability framework using blockchain. We show that the proposed transparency framework is effective and guarantees security properties. Experimental evaluation shows that the proposed framework is efficient.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Xu, Runhuarunhua.xu@pitt.edurux90000-0003-4541-9764
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairJoshi, Jamesjjoshi@pitt.edujjoshi
Committee MemberKrishnamurthy, Prashantprashk@pitt.eduprashk
Committee MemberPalanisamy, BalajiBPALAN@pitt.edubpalan
Committee MemberBaracaldo,
Date: 20 August 2020
Date Type: Publication
Defense Date: 3 August 2020
Approval Date: 20 August 2020
Submission Date: 7 August 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 141
Institution: University of Pittsburgh
Schools and Programs: School of Computing and Information > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: secure computation; functional encryption; privacy-preserving machine learning; blockchain
Date Deposited: 20 Aug 2020 18:51
Last Modified: 20 Aug 2020 18:51


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