Privacy-preserving smart metering

By Alfredo Rial, George Danezis. 2011

382 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Geppetto: Versatile Verifiable Computation (2015, background): “For instance, a MapReduce job may need to compute over signed data, or a customer with a smart meter may wish to privately compute a bill over signed readings [48].”
Pinocchio: Nearly Practical Verifiable Computation (2013, background): “Rial and Danezis [32] propose a system for privacypreserving smart metering in which clients use a ZK protocol to prove correctness of the billing computation they perform on meter readings.”
A Decentralized Security Framework for Data Aggregation and Access Control in Smart Grids (2013, background): “Rial and Danesiz [17] proposed a privacy preserving protocol for smart meters using zero knowledge proof [18], that ensures correct payment of fees without disclosing the details about information on electricity consumption.”
Privacy-Enhanced Data Aggregation Scheme Against Internal Attackers in Smart Grid (2014, background): “In addition, time-of-use billing is another crucial reason for collecting and storing all power consumption data [7], [8].”
ADSNARK: Nearly Practical and Privacy-Preserving Proofs on Authenticated Data (2015, methodology): “, in [5, 6], and [7], respectively (and also for other applications of similarly low complexity).”
Privacy-Friendly Aggregation for the Smart-Grid (2011, background): “Rial and Danezis [6] propose a protocol using commitments and zero knowledge proofs to privately derive and prove the correctness of bills, but not for aggregation across meters.”
Security and Privacy Issues of Fog Computing: A Survey (2015, background): “Privacy-preserving techniques have been proposed in many scenarios including cloud [4], smart grid [28], wireless network [27] , and online social network [24].”
A Survey of Fog Computing: Concepts, Applications and Issues (2015, background): “Privacy-preserving techniques have been proposed in many scenarios including cloud [48, 4], smart grid [40], wireless network [39] , and online social network [35].”
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].”
I Have a DREAM! (DiffeRentially privatE smArt Metering) (2011, background): “Several papers addressed the privacy problems of smart mete ring in the recent past [8, 15, 2, 16, 3, 4, 18, 10].”
A Multi-Sensor Energy Theft Detection Framework for Advanced Metering Infrastructures (2013, background): “While we defer the design of privacy-preserving protocols [27] for our scheme to future work, we mention a practical measure to mitigate leaks of most legitimate user’s consumption patterns.”
Smart meter aggregation via secret-sharing (2013, background): “Thus we can very cheaply acquire a supply of randomness to cover all use-cases where in past proposals computation was offloaded to a user device [21].”
Verified Computational Differential Privacy with Applications to Smart Metering (2013, methodology): “Another challenge is to provide tool-support for reasoning about the utility of differentially private algorithms, which would require more advanced methods to derive bounds about the probability of events w.r.t. distributions generated by programs (e.g. Chebyshev’s inequality).”
Fault-Tolerant Privacy-Preserving Statistics (2012, methodology): “Another cryptographic approach very similar to [12] is described in [24].”
A Survey of Verifiable Computation (2017, background): “Rial and Danezis [75] proposed a scheme to achieve privacy preservation of smart metering by using a ZK protocol to verify the correctness of billing computation on meter readings.”
Chainspace: A Sharded Smart Contracts Platform (2017, methodology): “We implement a basic private smart-meter billing mechanism [14, 28] using the contract SMet: it implements three types SMet.”