TY - GEN
T1 - Privacy Protection in Smart Health
AU - Arca, Sevgi
AU - Hewett, Rattikorn
N1 - Publisher Copyright:
© 2020 ACM.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - Smart health exploits smart health devices (e.g., fitness trackers, heart rate or glucose monitoring units) and Internet of Things technologies to improve users' health and wellness. By enabling self-monitoring and data sharing among users and healthcare professions, smart health can increase healthy habits, timely treatments, reduce hospital visits/re-admissions and even save lives. While smart health comes with great benefits, it also poses a privacy threat to the re-identification of users and their personal data. This paper presents an approach to protecting users' privacy by generalizing critical data so that they belong to multiple users as a way to anonymize user identity. Unlike existing anonymization techniques, our approach efficiently produces shared data that satisfy user-specified anonymity requirements while keeping the data as informative as possible. The approach is based on an Artificial Intelligence search technique using two proposed heuristics. The paper describes and illustrates the approach with experiments to compare its effectiveness with other techniques. The results show that, given a trade-off of privacy preserving, data retention and computational cost, our approach gives the most effective solution for data sharing as expected.
AB - Smart health exploits smart health devices (e.g., fitness trackers, heart rate or glucose monitoring units) and Internet of Things technologies to improve users' health and wellness. By enabling self-monitoring and data sharing among users and healthcare professions, smart health can increase healthy habits, timely treatments, reduce hospital visits/re-admissions and even save lives. While smart health comes with great benefits, it also poses a privacy threat to the re-identification of users and their personal data. This paper presents an approach to protecting users' privacy by generalizing critical data so that they belong to multiple users as a way to anonymize user identity. Unlike existing anonymization techniques, our approach efficiently produces shared data that satisfy user-specified anonymity requirements while keeping the data as informative as possible. The approach is based on an Artificial Intelligence search technique using two proposed heuristics. The paper describes and illustrates the approach with experiments to compare its effectiveness with other techniques. The results show that, given a trade-off of privacy preserving, data retention and computational cost, our approach gives the most effective solution for data sharing as expected.
KW - Anonymization
KW - privacy protection
KW - smart health
UR - http://www.scopus.com/inward/record.url?scp=85089182555&partnerID=8YFLogxK
U2 - 10.1145/3406601.3406620
DO - 10.1145/3406601.3406620
M3 - Conference contribution
AN - SCOPUS:85089182555
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 11th International Conference on Advances in Information Technology, IAIT 2020
PB - Association for Computing Machinery
Y2 - 1 July 2020 through 3 July 2020
ER -