Privacy-Preserving Aggregation of Time-Series Data

By Elaine Shi, T-H. Hubert Chan, E. Rieffel, Richard Chow, Dawn Song. 2011

653 citations recorded by the source corpus.

Subfields: Privacy & Security, Time-Series & Forecasting

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In Time-Series & Forecasting

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 [”
Privacy Amplification by Decentralization (2020, background): “A popular approach is to resort to cryptographic primitives to securely aggregate user contributions [14, 31, 7, 10, 21].”
Private Summation in the Multi-Message Shuffle Model (2020, methodology): “[21], we exploit the infinite divisibility properties of the geometric distribution, as suggested by Goryczka and Xiong [16].”
Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, background): “Additional theoretical contributions have also operated in the local setting, but have included additional cryptographic tools [19, 46, 70].”
Distributed Differential Privacy via Shuffling (2018, background): “…either (1) focus on simple summations and require a single “semi-honest”/“honest-but-curious” server that aggregates user answers, as in Shi et al. [25], Chan et al. [10], Bonawitz et al. [7] ; or (2) allow general computations, but require a network of servers, a majority of whom are assumed to…”
Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, methodology): “By carefully adding structured noise to the final sum, these systems can provide differential privacy as well [56, 92, 107].”
A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, methodology): “also utilized a homomorphic encryption scheme, and minimized its communication complexity by generating an encryption key from the current round number and the existing key [50].”
Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries (2015, background): “Research on local differential privacy has largely been centered around finding algorithms that satisfy differential privacy properties [19, 21, 22], and improving the tradeoffs between privacy and utility [7, 14].”
Efficient Private Statistics with Succinct Sketches (2015, background): “Shi et al. [61] combine private aggregation with differential privacy supporting the aggregation of encrypted perturbed readings reported by the meters.”
Technical Privacy Metrics (2015, background): “4 Distributed differential privacy Smart metering [116] 5.”
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].”
Privacy-Preserving Stream Aggregation with Fault Tolerance (2012, methodology): “Recently, the research community has started to consider how to guarantee differential privacy in the presence of an untrusted aggregator [13,14].”
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.”
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, methodology): “Rastogi and Nath [37] use an algorithm based on Discrete Fourier Transform to privately release aggregate time-series, while Shi et al. [38] combine encryption with data randomization to achieve differential privacy for time series data.”
I Have a DREAM! (DiffeRentially privatE smArt Metering) (2011, methodology): “In addition, [23] is not robust against node and communication failures.”
Non-tracking web analytics (2012, background): “Although attempts have been made to provide differential privacy in a distributed environment, these attempts either incur high overhead [36] or suffer from client churn [47,50], making them impractical in a large-scale environment.”