Distributed Private Heavy Hitters

@inproceedings{Hsu2012DistributedPH,
  title={Distributed Private Heavy Hitters},
  author={Justin Hsu and S. Khanna and Aaron Roth},
  booktitle={ICALP},
  year={2012}
}
In this paper, we give efficient algorithms and lower bounds for solving the heavy hitters problem while preserving differential privacy in the fully distributed local model. In this model, there are n parties, each of which possesses a single element from a universe of size N. The heavy hitters problem is to find the identity of the most common element shared amongst the n parties. In the local model, there is no trusted database administrator, and so the algorithm must interact with each of… Expand
Heavy Hitters and the Structure of Local Privacy
We present a new locally differentially private algorithm for the heavy hitters problem which achieves optimal worst-case error as a function of all standardly considered parameters. Prior workExpand
Heavy Hitters and the Structure of Local Privacy
TLDR
A new locally differentially private algorithm for the heavy hitters problem that achieves optimal worst-case error as a function of all standardly considered parameters is presented and shows that you cannot obtain more accurate algorithms by moving from pure to approximate local privacy. Expand
Private Heavy Hitters and Range Queries in the Shuffled Model
TLDR
This work studies two basic statistical problems, namely, heavy hitters and d-dimensional range counting queries, in the shuffled model of privacy, and devise algorithms with polylogarithmic communication per user and polylogARithmic error. Expand
Local, Private, Efficient Protocols for Succinct Histograms
TLDR
Efficient protocols and matching accuracy lower bounds for frequency estimation in the local model for differential privacy are given and it is shown that each user need only send 1 bit to the server in a model with public coins. Expand
Differentially Private k-Means with Constant Multiplicative Error
TLDR
This work designs new differentially private algorithms for the Euclidean k-means problem, both in the centralized model and in the local model of differential privacy, achieving significantly improved error guarantees than the previous state-of-the-art. Expand
Secure Multi-party Computation of Differentially Private Median
TLDR
A multi-party computation to efficiently compute the exponential mechanism for the median, which also supports, e.g, general rank-based statistics (e.g., pthpercentile, interquartile range) and convex optimizations for machine learning. Expand
Locally Differentially Private Heavy Hitter Identification
TLDR
In this paper, a proposed LDP protocol, which the authors call Prefix Extending Method (PEM), users are divided into groups, with each group reporting a prefix of her value and experiments show that under the same privacy guarantee and computational cost, PEM has better utility on both synthetic and real-world datasets than existing solutions. Expand
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
TLDR
This work proposes a sample-optimal $\varepsilon$-locally differentially private (LDP) scheme for distribution estimation, where each user communicates only one bit, and requires no public randomness. Expand
Manipulation Attacks in Local Differential Privacy
TLDR
It is shown that any non-interactive locally differentially private protocol can be manipulated to a much greater extent when the privacy level is high or the input domain is large, and the importance of efficient cryptographic techniques for emulating mechanisms from central differential privacy in distributed settings is reinforced. Expand
Multi-Central Differential Privacy
TLDR
An intermediate trust model for differential privacy, which is called the multi-central model, where there are multiple aggregators and it is argued that this model is a promising direction for further research. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 26 REFERENCES
The Limits of Two-Party Differential Privacy
TLDR
Borders expose a dramatic gap between the accuracy that can be obtained by differentially private data analysis versus the accuracy obtainable when privacy is relaxed to a computational variant of differential privacy. Expand
Iterative Constructions and Private Data Release
TLDR
New algorithms (and new analyses of existing algorithms) in both the interactive and non-interactive settings are given, and a reduction based on the IDC framework shows that an efficient, private algorithm for computing sufficiently accurate rank-1 matrix approximations would lead to an improved efficient algorithm for releasing private synthetic data for graph cuts. Expand
Distributed Private Data Analysis: On Simultaneously Solving How and What
TLDR
The combination of two directions in the field of privacy concerning computations over distributed private inputs --- secure function evaluation (SFE) and differential privacy is examined, yielding new separations between the local and global models of computations for private data analysis. Expand
Fast Private Data Release Algorithms for Sparse Queries
TLDR
This paper considers the large class of sparse queries, which take non-zero values on only polynomially many universe elements, and gives efficient query release algorithms for this class, in both the interactive and the non-interactive setting. Expand
Mechanism Design via Differential Privacy
  • F. McSherry, Kunal Talwar
  • Computer Science
  • 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07)
  • 2007
TLDR
It is shown that the recent notion of differential privacv, in addition to its own intrinsic virtue, can ensure that participants have limited effect on the outcome of the mechanism, and as a consequence have limited incentive to lie. Expand
Differentially private combinatorial optimization
TLDR
It is shown that many such problems indeed have good approximation algorithms that preserve differential privacy, even in cases where it is impossible to preserve cryptographic definitions of privacy while computing any non-trivial approximation to even the value of an optimal solution, let alone the entire solution. Expand
On the geometry of differential privacy
TLDR
The lower bound is strong enough to separate the concept of differential privacy from the notion of approximate differential privacy where an upper bound of O(√{d}/ε) can be achieved. Expand
Pan-private algorithms via statistics on sketches
TLDR
This work presents the first known lower bounds explicitly for pan privacy, stronger than those implied by differential privacy or dynamic data streaming alone and hold even if unbounded memory and/or unbounded processing time are allowed. Expand
Differential Privacy: A Survey of Results
TLDR
This survey recalls the definition of differential privacy and two basic techniques for achieving it, and shows some interesting applications of these techniques, presenting algorithms for three specific tasks and three general results on differentially private learning. Expand
Compressive mechanism: utilizing sparse representation in differential privacy
TLDR
The amount of noise is significantly reduced when the noise insertion procedure is carried on the synopsis samples instead of the original database, and the proposed compressive mechanism is applied to solve the problem of continual release of statistical results. Expand
...
1
2
3
...