TY - GEN
T1 - Personal Data for Personal Use
T2 - 2023 ACM/SIGMOD International Conference on Management of Data, SIGMOD 2023
AU - Dong, Xin Luna
AU - Li, Bo
AU - Stoyanovich, Julia
AU - Tung, Anthony Kum Hoe
AU - Weikum, Gerhard
AU - Halevy, Alon
AU - Tan, Wang Chiew
N1 - Funding Information:
Julia Stoyanovich is Institute Associate Professor of Computer Science and Engineering, Associate Professor of Data Science, and Director of the Center for Responsible AI at New York University. Her goal is to make “Responsible AI” synonymous with “AI”. She works towards this goal by engaging in academic research, education and technology regulation, and by speaking about the benefits and harms of AI to practitioners and members of the public. Julia’s research focuses on responsible data management and analysis: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data science lifecycle. She developed and has been teaching courses on Responsible Data Science at NYU, and is a co-creator of an award-winning comic book series on this topic. In addition to responsible data science, Julia works on the management and analysis of preference and voting data, and on querying large evolving graphs. She holds M.S. and Ph.D. degrees in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics and Statistics from the University of Massachusetts at Amherst. She is a recipient of an NSF CAREER award and a Senior Member of the ACM.
Funding Information:
Bo Li is an assistant professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, AI’s 10 to Watch, NSF CAREER Award, MIT Technology Review TR-35 Award, Dean’s Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star award, Symantec Research Labs Fellowship, Rising Star Award, Research Awards from Tech companies such as Amazon, Facebook, Intel, IBM, and eBay, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, which is at the intersection of machine learning, security, privacy, and game theory. She has designed several scalable frameworks for trustworthy machine learning and privacy-preserving data publishing.
Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/6/4
Y1 - 2023/6/4
N2 - The vision of collecting all of one's personal information into one searchable database has been around at least since Vannevar Bush's 1945 paper on the Memex System [2]. In the late 1990's, Gordon Bell and his colleagues at Microsoft Research built MyLifeBits [1, 6], which was the first serious attempt to build such a database. Since then, there has been continued interest in our community to build personal information management systems [3-5, 7, 8, 10]. Recently, the Solid Project proposes a more radical approach to personal information, arguing that all of one's data should reside in their own data pod, and applications should be redesigned to fetch data from the pod [9].
AB - The vision of collecting all of one's personal information into one searchable database has been around at least since Vannevar Bush's 1945 paper on the Memex System [2]. In the late 1990's, Gordon Bell and his colleagues at Microsoft Research built MyLifeBits [1, 6], which was the first serious attempt to build such a database. Since then, there has been continued interest in our community to build personal information management systems [3-5, 7, 8, 10]. Recently, the Solid Project proposes a more radical approach to personal information, arguing that all of one's data should reside in their own data pod, and applications should be redesigned to fetch data from the pod [9].
KW - artificial intelligence
KW - personal information management
UR - http://www.scopus.com/inward/record.url?scp=85162908516&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85162908516&partnerID=8YFLogxK
U2 - 10.1145/3555041.3589378
DO - 10.1145/3555041.3589378
M3 - Conference contribution
AN - SCOPUS:85162908516
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 263
EP - 264
BT - SIGMOD 2023 - Companion of the 2023 ACM/SIGMOD International Conference on Management of Data
PB - Association for Computing Machinery
Y2 - 18 June 2023 through 23 June 2023
ER -