TY - GEN
T1 - Data Level Privacy Preserving
T2 - 40th IEEE International Conference on Data Engineering, ICDE 2024
AU - Ma, Chuan
AU - Yuan, Long
AU - Han, Li
AU - Ding, Ming
AU - Bhaskar, Raghav
AU - Li, Jun
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - With the great amount of available data, especially collected from the ubiquitous Internet of Things (IoT), the issue of privacy leakage has been an increasing concern recently. To preserve the privacy of IoT datasets, traditional methods usually calibrate random noises on the data values to achieve differential privacy (DP) [1]. However, the amount of calibrating noises should be carefully designed and a heedless value will definitely degrade the availability of datasets.
AB - With the great amount of available data, especially collected from the ubiquitous Internet of Things (IoT), the issue of privacy leakage has been an increasing concern recently. To preserve the privacy of IoT datasets, traditional methods usually calibrate random noises on the data values to achieve differential privacy (DP) [1]. However, the amount of calibrating noises should be carefully designed and a heedless value will definitely degrade the availability of datasets.
KW - Differential Privacy
KW - Stochastic Perturbation
KW - Tabular Dataset
UR - https://www.scopus.com/pages/publications/85200517896
U2 - 10.1109/ICDE60146.2024.00492
DO - 10.1109/ICDE60146.2024.00492
M3 - 会议稿件
AN - SCOPUS:85200517896
T3 - Proceedings - International Conference on Data Engineering
SP - 5721
EP - 5722
BT - Proceedings - 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024
PB - IEEE Computer Society
Y2 - 13 May 2024 through 17 May 2024
ER -