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Title: A Multi-Disciplinary Perspective for Conducting Artificial Intelligence-enabled Privacy Analytics: Connecting Data, Algorithms, and Systems
Events such as Facebook-Cambridge Analytica scandal and data aggregation efforts by technology providers have illustrated how fragile modern society is to privacy violations. Internationally recognized entities such as the National Science Foundation (NSF) have indicated that Artificial Intelligence (AI)-enabled models, artifacts, and systems can efficiently and effectively sift through large quantities of data from legal documents, social media, Dark Web sites, and other sources to curb privacy violations. Yet considerable efforts are still required for understanding prevailing data sources, systematically developing AI-enabled privacy analytics to tackle emerging challenges, and deploying systems to address critical privacy needs. To this end, we provide an overview of prevailing data sources that can support AI-enabled privacy analytics; a multi-disciplinary research framework that connects data, algorithms, and systems to tackle emerging AI-enabled privacy analytics challenges such as entity resolution, privacy assistance systems, privacy risk modeling, and more; a summary of selected funding sources to support high-impact privacy analytics research; and an overview of prevailing conference and journal venues that can be leveraged to share and archive privacy analytics research. We conclude this paper with an introduction of the papers included in this special issue.
Authors:
; ;
Award ID(s):
1917117 2041770 2038483 1936370
Publication Date:
NSF-PAR ID:
10252103
Journal Name:
ACM Transactions on Management Information Systems
Volume:
12
Issue:
1
Page Range or eLocation-ID:
1 to 18
ISSN:
2158-656X
Sponsoring Org:
National Science Foundation
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