• Corpus ID: 148567614

Bootstrap Differential Privacy

  title={Bootstrap Differential Privacy},
  author={Christine M. O'Keefe and Anne-Sophie Charest},
  journal={Trans. Data Priv.},
This paper concerns the challenge of protecting confidentiality while making statistically useful data and analytical outputs available for research and policy analysis. In this context, the confidentiality protection measure known as differential privacy is an attractive methodology because of its clear definition and the strong guarantees that it promises. However, concerns about differential privacy include the possibility that in some situations the guarantees may be so strong that… 
1 Citations

Figures and Tables from this paper

Reconstruction Attacks on Aggressive Relaxations of Differential Privacy

This work designs a set of queries that, when protected by these mechanisms with high noise set-tings, yield more precise information about the dataset than if they were not protected at all, and demonstrates these attacks using the preferred mechanisms of these privacy definitions.



Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees

This paper argues that the standard formalization of differential privacy is stricter than required by the intuitive privacy guarantee it seeks, and proposes individual differential privacy, an alternative differential privacy notion that offers the same privacy guarantees as standard differential privacy to individuals (even though not to groups of individuals).

Towards a Systematic Analysis of Privacy Definitions

A novel methodology for analyzing the Bayesian properties of a privacy definition is added, its goal is to identify precisely the type of information being protected, hence making it easier to identify (and later remove) unnecessary data protections.

Privacy, accuracy, and consistency too: a holistic solution to contingency table release

This work proposes a solution that provides strong guarantees for all three desiderata simultaneously, privacy, accuracy, and consistency among the tables, and applies equally well to the logical cousin of the contingency table, the OLAP cube.

The Algorithmic Foundations of Differential Privacy

The preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example.

On the Meaning and Limits of Empirical Differential Privacy

It is shown that EDP is not well-defined, in that its value depends crucially on the choice of discretization used in the procedure, and that it can be very computationnaly intensive to apply in practice.

Differential Privacy

  • C. Dwork
  • Computer Science
    Encyclopedia of Cryptography and Security
  • 2006
A general impossibility result is given showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved, which suggests a new measure, differential privacy, which, intuitively, captures the increased risk to one's privacy incurred by participating in a database.

Smooth sensitivity and sampling in private data analysis

This is the first formal analysis of the effect of instance-based noise in the context of data privacy, and shows how to do this efficiently for several different functions, including the median and the cost of the minimum spanning tree.

Calibrating Noise to Sensitivity in Private Data Analysis

The study is extended to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f, which is the amount that any single argument to f can change its output.

Privacy: Theory meets Practice on the Map

In this paper, we propose the first formal privacy analysis of a data anonymization process known as the synthetic data generation, a technique becoming popular in the statistics community. The

Random Differential Privacy

It is shown that RDP histograms are much more accurate than histograms obtained using ordinary differential privacy, and an analog of the global sensitivity framework for the release of functions under the privacy definition is shown.