Differential Privacy versus Quantitative Information Flow
@article{Alvim2010DifferentialPV,
title={Differential Privacy versus Quantitative Information Flow},
author={M{\'a}rio S. Alvim and Konstantinos Chatzikokolakis and Pierpaolo Degano and Catuscia Palamidessi},
journal={ArXiv},
year={2010},
volume={abs/1012.4250}
}Differential privacy is a notion of privacy that has become very popular in the database community. Roughly, the idea is that a randomized query mechanism provides sufficient privacy protection if the ratio between the probabilities of two different entries to originate a certain answer is bound by e^e. In the fields of anonymity and information flow there is a similar concern for controlling information leakage, i.e. limiting the possibility of inferring the secret information from the…
17 Citations
Differential Privacy: On the Trade-Off between Utility and Information Leakage
- Computer ScienceFormal Aspects in Security and Trust
- 2011
It is shown that the notion of differential privacy implies a bound on utility, also tight, and a method is proposed that under certain conditions builds an optimal randomization mechanism, i.e. a mechanism which provides the best utility while guaranteeing differential privacy.
On the Relation between Differential Privacy and Quantitative Information Flow
- Computer ScienceICALP
- 2011
This paper analyzes critically the notion of differential privacy in light of the conceptual framework provided by the Renyi min information theory, and shows that there is a close relation between differential privacy and leakage, due to the graph symmetries induced by the adjacency relation.
Quantitative Information Flow and Applications to Differential Privacy
- Computer ScienceFOSAD
- 2011
The bound that differential privacy induces on leakage, and the trade-off between utility and privacy are discussed, using the information-theoretic view.
On the information leakage of differentially-private mechanisms
- Computer ScienceJ. Comput. Secur.
- 2015
Differential privacy aims at protecting the privacy of participants in
statistical databases. Roughly, a mechanism satisfies differential privacy if
the presence or value of a single individual in…
Quantitative Information Flow and applications
- Computer Science
- 2011
Using the information-theoretic view, the bound that dierential privacy induces on leakage, and the trade-o between utility and privacy are discussed, are discussed.
Information-Theoretic Bounds for Differentially Private Mechanisms
- Computer Science2011 IEEE 24th Computer Security Foundations Symposium
- 2011
The purpose of this article is to establish formal connections between both notions of confidentiality, and to compare them in terms of the security guarantees they deliver, and it is shown that the question of providing optimal upper bounds for the leakage of eps-differentially private mechanisms in Terms of rational functions of ePS is in fact decidable.
Model for a Common Notion of Privacy Leakage on Public Database
- Computer ScienceJ. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl.
- 2011
A unified model is proposed that is based on the perturbation method, but which is applicable to generalized data sets, and applies the notion of differential privacy to data sets that satisfy k-anonymity.
On Privacy and Accuracy in Data Releases
- Computer Science
- 2020
A model of quantitative information flow is used to describe the the trade-off between privacy of individuals’ data and and the utility of queries to that data by modelling the effectiveness of adversaries attempting to make inferences after a data release.
Differential privacy with information flow control
- Computer Science, MathematicsPLAS '11
- 2011
It is found that information flow for differentiallyPrivate observations is no harder than dependency tracking, allowing the use of existing technology to extend and improve the state of the art for the analysis of differentially private computations.
Common Criterion of Privacy Metrics and Parameters Analysis Based on Error Probability for Randomized Response
- Computer ScienceIEEE Access
- 2019
The error probability is proposed as a measure of data utility for unifying different privacy metrics, where the error probabilities can be applied to any prior distribution case, while the average distortion criterion is only a special case with uniform distribution.
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