Weining Yang - Purdue University
All seminars for Spring 2021 will be held virtually. (No in-person classroom)
Minimizing Private Data Disclosures in the Smart Grid
Feb 20, 2013Download: MP4 Video Size: 104.3MB
Watch on YouTube
AbstractSmart electric meters are meters that can measure electric usage with a pretty high frequency. Smart electric meters pose a substantial threat to the privacy of individuals in their own homes. Combined with a method called non-intrusive load monitors, smart meter data can reveal precise home appliance usage information. An emerging solution to behavior leakage in smart meter measurement data is the use of battery-based load hiding. In this approach, a battery is used to store and supply power to home devices at strategic times to hide appliance loads from smart meters. A few such battery control algorithms have already been studied in the literature.
In this talk, we will ﬁrst consider two well known battery privacy algorithms, Best Effort (BE) and Non-Intrusive Load Leveling (NILL), and demonstrate attacks that recover precise load change information, which can be used to recover appliance behavior information, under both algorithms. We will then introduce a stepping approach to battery privacy algorithms that fundamentally differs from previous approaches by maximizing the error between the load demanded by a home and the external load seen by a smart meter. By design, precise load change recovery attacks are impossible. We also propose mutual-information based measurements to evaluate the privacy of different algorithms. We implement and evaluate four novel algorithms using the stepping approach, and show that under the mutual-information metrics they outperform BE and NILL
About the Speaker
Weining Yang is a PhD student in the Computer Science department of Purdue University. He received his Bachelor's degree in Computer Science and Technology in 2011 from Tsinghua University. His research interests are information security and data privacy. In particular, his research focuses on privacy preserving data publishing. His research advisor is Prof. Ninghui Li.