TY - JOUR
T1 - A Multi-Disciplinary Perspective for Conducting Artificial Intelligence-enabled Privacy Analytics
T2 - Connecting Data, Algorithms, and Systems
AU - Samtani, Sagar
AU - Kantarcioglu, Murat
AU - Chen, Hsinchun
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant Numbers OAC-1917117 (CICI), CNS-1936370 (SaTC CORE), CNS-1850362 (CRII SaTC), and DGE-2038483 (SaTC-EDU). Authors’ addresses: S. Samtani (corresponding author), Department of Operations and Decision Technologies, Indiana University, 1275 E. 10th St., Bloomington, Indiana 47405, USA; email: ssamtani@iu.edu; M. Kantarcioglu, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, 800 W. Campbell Rd., Richardson, TX 75080, USA; email: muratk@utdallas.edu; H. Chen, Department of Management Information Systems, University of Arizona, 1130 E. Helen St., McClelland Hall 430, Tucson, AZ 85721, USA; email: hsinchun@arizona.edu. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). © 2021 Copyright held by the owner/author(s). 2158-656X/2021/03-ART1 https://doi.org/10.1145/3447507
Publisher Copyright:
© 2021 ACM.
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
KW - Privacy
KW - analytics
KW - artificial intelligence
KW - data
KW - systems
KW - theories
UR - http://www.scopus.com/inward/record.url?scp=85102982627&partnerID=8YFLogxK
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U2 - 10.1145/3447507
DO - 10.1145/3447507
M3 - Article
AN - SCOPUS:85102982627
VL - 12
JO - ACM Transactions on Management Information Systems
JF - ACM Transactions on Management Information Systems
SN - 2158-656X
IS - 1
M1 - 3447507
ER -