TY - GEN
T1 - Leveraging Privacy-Enhancing Technology to Better Serve the United States' Public
AU - Chisolm-Straker, Makini
AU - Varshney, Lav R.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - While the public views the federal government as a monolith, in truth United States departments and agencies often function independently. Due to privacy regulations and statutory reasons, they often cannot share data about individuals with each other. Yet, such data collaboration would facilitate the development of artificial intelligence models to significantly improve the provision of social services. We argue that a particular privacy-enhancing technology (split learning operating over vertically partitioned data), together with data governance through metadata supply chains, can be part of larger sociotechnical systems that can improve life for the most vulnerable among us.
AB - While the public views the federal government as a monolith, in truth United States departments and agencies often function independently. Due to privacy regulations and statutory reasons, they often cannot share data about individuals with each other. Yet, such data collaboration would facilitate the development of artificial intelligence models to significantly improve the provision of social services. We argue that a particular privacy-enhancing technology (split learning operating over vertically partitioned data), together with data governance through metadata supply chains, can be part of larger sociotechnical systems that can improve life for the most vulnerable among us.
UR - http://www.scopus.com/inward/record.url?scp=85218040475&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85218040475&partnerID=8YFLogxK
U2 - 10.1109/BigData62323.2024.10824997
DO - 10.1109/BigData62323.2024.10824997
M3 - Conference contribution
AN - SCOPUS:85218040475
T3 - Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
SP - 2294
EP - 2296
BT - Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
A2 - Ding, Wei
A2 - Lu, Chang-Tien
A2 - Wang, Fusheng
A2 - Di, Liping
A2 - Wu, Kesheng
A2 - Huan, Jun
A2 - Nambiar, Raghu
A2 - Li, Jundong
A2 - Ilievski, Filip
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Big Data, BigData 2024
Y2 - 15 December 2024 through 18 December 2024
ER -