Fault-Tolerant Privacy-Preserving Statistics

By Marek Jawurek, Florian Kerschbaum. 2012

78 citations recorded by the source corpus.

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In Artificial Intelligence

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, background): “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, background): “This allows us to seamlessly tolerate missing contributions – following an approached ﬁrst proposed by Jawurek et al. [45].”
Smart meter aggregation via secret-sharing (2013, methodology): “Yet, it uses a similar security model as previous mechanisms [11, 15, 17] with multiple honest-butcurious parties.”
A Scalable Scheme for Privacy-Preserving Aggregation of Time-Series Data (2013, background): “In a recent work, Jawurek and Kerschbaum [17] managed to avoid these overheads in a protocol that further computes weighted sums.”
Rmind: A Tool for Cryptographically Secure Statistical Analysis (2016, background): “g, mean, variance, frequency analysis and regression [16, 21, 22, 41, 40], filtered sums, weighted sums and scalar products [53, 56, 36].”
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].”
Private and Dynamic Time-Series Data Aggregation with Trust Relaxation (2014, background): “Jawurek and Kerschbaum [7] eliminate this extra communication round between the users and the aggregator by introducing a key manager which unfortunately can decrypt users’ individual data.”
Privacy and Data Protection by Design - from policy to engineering (2014, background): “Example protocols for computing simple statistics for smart metering deployments have been proposed and implemented [128], or even for training and evaluating simple regression models [109].”
Differentially private data aggregation with optimal utility (2014, background): “The former rely on trusted or honest-but-curious (HbC) 1 servers [6, 15, 32] to compute noise perturbation.”
Achieving Differential Privacy in Secure Multiparty Data Aggregation Protocols on Star Networks (2017, background): “Jawurek and Kerschbaum [11] Practical scheme with a single additively homomorphic key-pair Star network + key managing authority Yes Yes Key managing authority knows the noise term in the final sum.”