Private memoirs of a smart meter

By Andres Molina-Markham, Prashant J. Shenoy, Kevin Fu, E. Cecchet, David E. Irwin. BuildSys@SenSys, 2010

573 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/042810cdcfb8f8af2f46e13543ccc9a3c9476f69

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

Engineering Privacy by Design (2011, methodology): “The design methodology is applicable to other services that bill users depending on their consumption, as for instance Smart Metering5 [40, 34].”
Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams (2018, background): “[27] propose a system that allows a smart meter to report its billing whiteout reporting its usage.”
User Perceptions of Smart Home IoT Privacy (2018, background): “Related work on smart home privacy has outlined technical and design challenges [4, 8] and revealed potential attacks to recognize users and behaviors from data collected from smart environments [2, 13, 37, 39, 50].”
Privacy-preserving smart metering (2011, background): “Molina et al. [32] highlight the private information that current meters leak, and sketch a protocol that uses zero-knowledge proofs to achieve privacy in metering .”
I Have a DREAM! (DiffeRentially privatE smArt Metering) (2011, background): “Several papers addressed the privacy problems of smart meters in the recent past [9, 19, 1, 2, 3, 21, 15, 12].”
Privacy-Friendly Aggregation for the Smart-Grid (2011, background): “et all [5], highlight the privacy related threats of smart metering and propose an architecture for secure measurements, that rely on trusted components outside of the meter.”
Cyber-Physical Systems Security—A Survey (2017, background): “In addition, it is also possible to deduce lifestyle in terms of sleeping times and quality, preferred home appliances, and many more [114].”
A Multi-Sensor Energy Theft Detection Framework for Advanced Metering Infrastructures (2013, background): “Recent studies have shown that NILMs can reveal home occupant behaviors [25], [26].”
Differentially private data aggregation with optimal utility (2014, background): “For example, researchers demonstrated how precise information about consumer habits can be reconstructed from the electricity consumption information collected by smart meters [36] and how an individual’s identity and state of health can be derived from genome wide association studies [40].”
Fault-Tolerant Privacy-Preserving Statistics (2012, background): “Attacks on smart metering privacy: Several works [8,9,15,16,17,19,22] have covered the area of behavior analysis from energy consumption traces.”