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  • Accepted Manuscript: Accessible Privacy-Preserving Web-Based Data Analysis for Assessing and Addressing Economic Inequalities
Title: Accessible Privacy-Preserving Web-Based Data Analysis for Assessing and Addressing Economic Inequalities
An essential component of initiatives that aim to address pervasive inequalities of any kind is the ability to collect empirical evidence of both the status quo baseline and of any improvement that can be attributed to prescribed and deployed interventions. Unfortunately, two substantial barriers can arise preventing the collection and analysis of such empirical evidence: (1) the sensitive nature of the data itself and (2) a lack of technical sophistication and infrastructure available to both an initiative’s beneficiaries and to those spearheading it. In the last few years, it has been shown that a cryptographic primitive called secure multi-party computation (MPC) can provide a natural technological resolution to this conundrum. MPC allows an otherwise disinterested third party to contribute its technical expertise and resources, to avoid incurring any additional liabilities itself, and (counterintuitively) to reduce the level of data exposure that existing parties must accept to achieve their data analysis goals. However, achieving these benefits requires the deliberate design of MPC tools and frameworks whose level of accessibility to non-technical users with limited infrastructure and expertise is state-of-the-art. We describe our own experiences designing, implementing, and deploying such usable web applications for secure data analysis within the context of two more » real-world initiatives that focus on promoting economic equality. « less
Authors:
; ; ; ; ; ;
Award ID(s):
1718135 1739000
Publication Date:
NSF-PAR ID:
10061831
Journal Name:
Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies
Page Range or eLocation-ID:
48:1-48:5
Sponsoring Org:
National Science Foundation
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