Smart meter privacy using a rechargeable battery: Minimizing the rate of information leakage
By David P. Varodayan, Ashish J. Khisti. 2011
162 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- George Pappas
- Flávio du Pin Calmon
- Andreas Pitsillides
- Nikos Komninos
- Jerome Le Ny
- Ram Rajagopal
- Praneeth Vepakomma
- Eleni Philippou
In Electrical and Electronic Engineering
- Lalitha Sankar
- Peter Kairouz
- Chong Huang
- Ram Rajagopal
- Xiao Chen
- Hadi Esmaeilzadeh
- George Pappas
- Flávio du Pin Calmon
Citations in the corpus
- Privacy in Deep Learning: A Survey (2020, background): “There is a body of work studying information-theoretic based methods for both privacy and fairness, where privacy and fairness are provided through information degradation, through obfuscation or adversarial learning and demonstrated by mutual information reduction [67, 68, 48, 69, 70, 71, 72, 73, 7”
- On the Robustness of Information-Theoretic Privacy Measures and Mechanisms (2018, background): “Varodayan and Khisti [46] and Tan et al.”
- Differentially private Kalman filtering (2012, unknown): “Recently, information-theoretic approaches have been proposed to guarantee some level of privacy when releasing time series [7], [8].”
- Context-Aware Generative Adversarial Privacy (2017, background): “Context-aware privacy notions have been so far studied by information theorists under the rubric of information theoretic (IT) privacy [3, 4, 5, 7, 9, 10, 11, 12, 13, 37, 38, 39, 42, 46, 54, 56, 58, 59, 60, 71, 79].”
- Differentially Private Filtering (2012, background): “Information-theoretic approaches have also been proposed to guarantee some level of privacy when releasing time series [17], [18].”
- Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures (2014, methodology): “…values randomly selected from the same gamma distribution: The final idea we present for the purposes of ensuring privacy, belongs to Varodayan et al. [49], who propose the use of a rechargeable battery to partially protect the privacy of information derived from a premise’s electrical load profile.”