Privacy-Friendly Aggregation for the Smart-Grid

By Klaus Kursawe, George Danezis, Markulf Kohlweiss. 2011

440 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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Citations in the corpus

Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, background): “Other privacy guarantees: Many existing systems for collecting sensitive data rely on secret-sharing [18, 44, 50], anonymizing networks [35, 51] or even systems like Tor [42, 63] to aggregate the data privately, but do not explicitly make use of differential privacy.”
Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, methodology): “Private data-collection systems [30, 48, 53, 56, 79, 86, 92] that use secret-sharing based methods to compute sums over private user data typically (a) provide no robustness guarantees in the face of malicious clients, (b) use expensive NIZKs to prevent client misbehavior, or (c) fail to defend priv”
Efficient Private Statistics with Succinct Sketches (2015, methodology): “[39], which is secure under the CDH assumption.”
Achieving Differential Privacy in Secure Multiparty Data Aggregation Protocols on Star Networks (2017, methodology): “[15] Private aggregation using additive secret sharing Star network + L “leaders”.”
On significance of the least significant bits for differential privacy (2012, background): “Examples of such mechanisms include diﬀer-entially private computations in a distributed setting [11], privacy-preserving billing and aggregation [5, 24].”
Fault-Tolerant Privacy-Preserving Statistics (2012, background): “Privacy-preserving smart metering statistics: Furthermore, we provide an overview on different (non-fault-tolerant) approaches that ensure privacy for different applications of smart metering data: leakage detection [5] by homomorphic encryption, general aggregation [14] and comparison by secret-sha”
PrivEx: Private Collection of Traffic Statistics for Anonymous Communication Networks (2014, methodology): “The secret sharing scheme is an adaption of the scheme presented by Barthe et al. [3] which itself is an extension of previous works by Kursawe et al. [20], Jawurek et al. [15] and Shi et al. [32].”
Two Is Not Enough: Privacy Assessment of Aggregation Schemes in Smart Metering (2017, methodology): “[7] presented four different aggregation based privacy mechanisms using various cryptographic approaches, while Lu et al.”
Smart meter aggregation via secret-sharing (2013, background): “previous mechanisms [11, 15, 17] with multiple honest-butcurious parties.”
Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams (2012, methodology): “In the remainder of this section, we will use this special encryption scheme as a blackbox – for more algebraic details on how these schemes are constructed, we refer the readers to [15, 20, 21].”
PUDA - Privacy and Unforgeability for Data Aggregation (2015, background): “In [12] the authors presented a solution for verifiable aggregation in case of untrustworthy users.”
A Scalable Scheme for Privacy-Preserving Aggregation of Time-Series Data (2013, background): “The fourth protocol of [19] has low overhead but, like the previous ones, it requires each user to store the fixed public key of all other users.”
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).”
Private and Dynamic Time-Series Data Aggregation with Trust Relaxation (2014, neutral): “A solution that blends multiparty computation with homomorphic encryption is also presented in [9], but contrary to our scheme it does not address the issue of dynamic group management.”