Mean estimation of electricity consumption data based on PID and local differential privacy
Author:
Affiliation:
1. Shanghai University of Electric Power, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3594692.3594698
Reference19 articles.
1. Privacy-Aware Smart Metering: Progress and Challenges
2. Molina-Markham A , Shenoy P , Fu K , Cecchet E and Irwin D 2010 Private memoirs of a smart meter In Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building 61-66 Molina-Markham A, Shenoy P, Fu K, Cecchet E and Irwin D 2010 Private memoirs of a smart meter In Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building 61-66
3. McKenna E , Richardson I , and Thomson M 2012 Smart meter data: Balancing consumer privacy concerns with legitimate applications Energy Policy 41 807-814 McKenna E, Richardson I, and Thomson M 2012 Smart meter data: Balancing consumer privacy concerns with legitimate applications Energy Policy 41 807-814
4. Asghar M R Dán G Miorandi D and Chlamtac I 2017 Smart meter data privacy: A survey IEEE Communications Surveys & Tutorials 19(4) 2820-2835 Asghar M R Dán G Miorandi D and Chlamtac I 2017 Smart meter data privacy: A survey IEEE Communications Surveys & Tutorials 19(4) 2820-2835
5. Cao H Liu S Guan Z Wu L Deng H and Du X 2018 An efficient privacy-preserving algorithm based on randomized response in IoT-based smart grid 2018 IEEE SmartWorld Ubiquitous Intelligence & Computing Advanced & Trusted Computing Scalable Computing & Communications Cloud & Big Data Computing Internet of People and Smart City Innovation 881-886 Cao H Liu S Guan Z Wu L Deng H and Du X 2018 An efficient privacy-preserving algorithm based on randomized response in IoT-based smart grid 2018 IEEE SmartWorld Ubiquitous Intelligence & Computing Advanced & Trusted Computing Scalable Computing & Communications Cloud & Big Data Computing Internet of People and Smart City Innovation 881-886
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