I Have a DREAM! (DiffeRentially privatE smArt Metering)

By Gergely Ács, Claude Castelluccia. 2011

451 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Privacy & Security

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

In Privacy & Security

Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “There is a rich literature exploring secure aggregation in both the single-server setting (via pairwise additive masking [12, 188, 73], via threshold homomorphic encryption [356, 193, 92], and via generic secure multi-party computation [86]) as well as in the multiple non-colluding servers setting [”
Byzantine-Resilient Secure Federated Learning (2020, methodology): “In the non-Byzantine federated learning setting, secure aggregation is performed through a procedure known as additive masking [2], [21].”
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020, methodology): “In [30], users agree on pairwise secret keys using a Diffie-Hellman type key exchange protocol and then each user sends the server a masked version of their data, which contains the pairwise masks as well as an individual mask.”
Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, methodology): “It uses pair-wise blinding to hide user inputs (as in [1, 38]), but does not focus on differential privacy.”
Practical Secure Aggregation for Federated Learning on User-Held Data (2016, background): “To construct perturbations, we assume a total ordering on U and take p u,v = PRG ( s u,v ) for u < v , p u,v = − PRG ( s u,v ) for u > v , and p u,v = 0 for u = v (as before).”
A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, methodology): “This mechanism utilizes infinite divisibility of Laplace distribution and generates partial noise by drawing it from the gamma distribution [2].”
A Hybrid Approach to Privacy-Preserving Federated Learning (2018, background): “Approaches for the private collection of streaming data, including [2, 8, 21, 37], aim to recover computation when one or more parties go down.”
Lightweight Crypto-Assisted Distributed Differential Privacy for Privacy-Preserving Distributed Learning (2020, background): “…necessitates mechanisms with stable distributions (like Gaussian distribution, Binomial distribution)—to guarantee proper calibration of known end-to-end response distribution— and cryptography for hiding all but the ﬁnal result from participants, as evidenced by [18], [19], [20], [21], [11].”
Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams (2018, background): “Acs and Casteluccia [24] propose a novel trustless scheme for light-weight smart meter which aggregates reading data of consuming devices and adds noise.”
Privacy and Robustness in Federated Learning: Attacks and Defenses (2020, background): “utility and ensure aggregator obliviousness, as evidenced in [21], [89], [90], [103], [104], [105], [106].”
Achieving Differential Privacy in Secure Multiparty Data Aggregation Protocols on Star Networks (2017, methodology): “Similarly, in Acs and Castelluccia [1] the participants generate Gammadistributed noise samples, which add up to a Laplace-distributed noise term at the aggregator.”
Combining Differential Privacy and Secure Multiparty Computation (2015, result): “Differentially private data aggregation has received a lot of interest [13, 1, 19], but typically without employing SMC.”
Private spatial data aggregation in the local setting (2016, background): “Acs and Castelluccia [15] introduce a novel cryptographic protocol for smart metering systems, whose key merit is in not requiring message exchange among users.”
Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, methodology): “In [148], real-time smart metering data is perturbed using Gamma distribution, and in order to make the aggregation secure, the authors used encrypted aggregation strategy.”
Two Is Not Enough: Privacy Assessment of Aggregation Schemes in Smart Metering (2017, background): “consumption using differential privacy have been proposed in [21][22][23][24].”
$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy (2016, unknown): “Ács and Castellucia [4] and”
Computational Differential Privacy from Lattice-Based Cryptography (2017, background): “Ács and Castelluccia [2] apply privacy-preserving data aggregation to smart metering.”
Secure multiparty aggregation with differential privacy: a comparative study (2013, methodology): “To ensure differential privacy in a distributed setting without any trusted aggregator, we evaluate existing distributed perturbation mechanisms, which are named after distributions of partial noise: Gamma [2] and Gauss [37].”
Differentially private data aggregation with optimal utility (2014, background): “Many schemes [5, 14, 38, 39] involve a signiﬁ-cant computational eﬀort on the user’s side, making them impractical in several scenarios, e.g., for aggregating data stored on mobile devices with limited computation power.”