TY - GEN
T1 - DPNCT
AU - Hafeez, Khadija
AU - Rehmani, Mubashir Husain
AU - O'Shea, Donna
N1 - Funding Information:
ACKNOWLEDGEMENT This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) and is funded under the Grant Number 18/CRT/6222.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - Highly accurate profiles of consumers daily energy usage are reported to power grid via smart meters which enables smart grid to effectively regulate power demand and supply. However, consumer's energy consumption pattern can reveal personal and sensitive information regarding their lifestyle. Therefore, to ensure users privacy, differentially distributed noise is added to the original data. This technique comes with a trade off between privacy of the consumer versus utility of the data in terms of providing services like billing, Demand Response schemes, and Load Monitoring. In this paper, we propose a technique - Differential Privacy with Noise Cancellation Technique (DPNCT) - to maximize utility in aggregated load monitoring and fair billing while preserving users' privacy by using noise cancellation mechanism on differentially private data. We introduce noise to the sensitive data stream before it leaves smart meters in order to guarantee privacy at individual level. Further, we evaluate the effects of different periodic noise cancelling schemes on privacy and utility i.e., billing and load monitoring. Our proposed scheme outperforms the existing scheme in terms of preserving the privacy while accurately calculating the bill.
AB - Highly accurate profiles of consumers daily energy usage are reported to power grid via smart meters which enables smart grid to effectively regulate power demand and supply. However, consumer's energy consumption pattern can reveal personal and sensitive information regarding their lifestyle. Therefore, to ensure users privacy, differentially distributed noise is added to the original data. This technique comes with a trade off between privacy of the consumer versus utility of the data in terms of providing services like billing, Demand Response schemes, and Load Monitoring. In this paper, we propose a technique - Differential Privacy with Noise Cancellation Technique (DPNCT) - to maximize utility in aggregated load monitoring and fair billing while preserving users' privacy by using noise cancellation mechanism on differentially private data. We introduce noise to the sensitive data stream before it leaves smart meters in order to guarantee privacy at individual level. Further, we evaluate the effects of different periodic noise cancelling schemes on privacy and utility i.e., billing and load monitoring. Our proposed scheme outperforms the existing scheme in terms of preserving the privacy while accurately calculating the bill.
KW - Demand Side Management (DSM)
KW - Differential Privacy (DP)
KW - Privacy Preservation
KW - Smart Grid (SG)
UR - http://www.scopus.com/inward/record.url?scp=85112801378&partnerID=8YFLogxK
U2 - 10.1109/ICCWorkshops50388.2021.9473837
DO - 10.1109/ICCWorkshops50388.2021.9473837
M3 - Conference contribution
AN - SCOPUS:85112801378
T3 - 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings
BT - 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 June 2021 through 23 June 2021
ER -
