Private and Continual Release of Statistics

By T-H. Hubert Chan, Elaine Shi, Dawn Song. 2010

607 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Statistics and Probability, Privacy & Security

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Citations in the corpus

Practical and Private (Deep) Learning without Sampling or Shuffling (2021, methodology): “ing the now standard tree aggregation protocol (Dwork et al., 2010; Chan et al., 2011).”
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].”
The Power of the Hybrid Model for Mean Estimation (2018, methodology): “First, we found that the bound on the concentration of the sum of Laplace random variables in [Chan et al., 2011] is nice to work with analytically, but is far from tight.”
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity (2018, methodology): “That work proposed a privacy-preserving mechanisms in the central model of differential privacy, later extended and applied by Chan et al. [CSS11] and Jain et al. [JKT12].”
Differential Privacy for Growing Databases (2018, background): “Both [DNPR10] and [CSS11] adapted the notion of diﬀer-ential privacy to streaming environments in a setting where each entry in the database is a single bit, and bits arrive one per unit time.”
Collecting Telemetry Data Privately (2017, background): “f work about streaming model of DP [14], the event-level private counting problem under continual observation is studied [11], with almost tight upper bounds in error (polynomial in logT) in [13] and [7]. [24] proposes a weaker protection for continual events in wconsecutive timestamps to remove the”
Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, background): “This is not surprising, as previous work highlights the difficulty of releasing private statistics under continual observation [6, 10, 17].”
Advanced Probabilistic Couplings for Differential Privacy (2016, neutral): “The differential literature proposes several interactive algorithms; examples include private counters [14, 21], the Sparse Vector mechanism, and other algorithms using these mechanisms [30].”
Make Up Your Mind: The Price of Online Queries in Differential Privacy (2016, methodology): “[2010a], Chan et al. [2011], Dwork et al. [2015b], we can answer k offline threshold queries on a dataset with only n = O(log k) elements, giving an exponential separation.”
Proving Differential Privacy in Hoare Logic (2014, background): “This demonstrates the simplicity offered by the self-product construction.”
Compressive mechanism: utilizing sparse representation in differential privacy (2011, background): “, wavelets [20], trees [12] and linear summation basis [14, 10, 5].”
The Role of Interactivity in Local Differential Privacy (2019, methodology): “1 (replacing concentration of randomized responses with concentration of Lap (1) noise [11]) it distinguishes between H0 and H1 with probability at least 2/3 using n = Ω ( 1 ε2α2 )”
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy (2014, methodology): “We have touched on a handful of private algorithms, including an algorithm for computing running sums [11] (part of a broader literature on streaming privacy), answering large classes of queries [25] (part of a broader literature on learning-theoretic approaches to data privacy).”
Differentially Private Filtering (2012, methodology): “As in [13], [14], two signals and are adjacent if and only if they differ by one at a single time, or equivalently”
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, methodology): “, without the need for trusted aggregators), and/or Differential Privacy (DP) is used to perturb aggregates [8, 14, 38, 39].”
Optimal private halfspace counting via discrepancy (2012, methodology): “A related algorithm that achieves an average squared error upper bound of O ((log n ) 3 d ) for d - dimensional orthogonal range counting was given by Chan, Shi, and Song [5].”
Locally Private Gaussian Estimation (2018, neutral): “[8] T.”