ISCAS OpenIR
Metrics for differential privacy in concurrent systems
Xu, Lili (1); Chatzikokolakis, Konstantinos (2); Lin, Huimin (4)
2014
Conference Name34th IFIPWG6.1 International Conference on Formal Techniques for Distributed Objects, Components, and Systems, FORTE 2014 - Held as Part of the 9th International Federated Conference on Distributed Computing Techniques, DisCoTec 2014
Pages199-215
Conference DateJune 3, 2014 - June 5, 2014
Conference PlaceBerlin, Germany
Indexed TypeEI
Publish PlaceSpringer Verlag
ISSN3029743
ISBN9783662436127
Department(1) INRIA, Paris, France; (2) CNRS, Paris, France; (3) Ecole Polytechnique, Paris, France; (4) Institute of Software, Chinese Academy of Sciences, Beijing, China; (5) Graduate University, Chinese Academy of Sciences, Beijing, China
English AbstractOriginally proposed for privacy protection in the context of statistical databases, differential privacy is now widely adopted in various models of computation. In this paper we investigate techniques for proving differential privacy in the context of concurrent systems. Our motivation stems from the work of Tschantz et al., who proposed a verification method based on proving the existence of a stratified family between states, that can track the privacy leakage, ensuring that it does not exceed a given leakage budget. We improve this technique by investigating a state property which is more permissive and still implies differential privacy. We consider two pseudometrics on probabilistic automata: The first one is essentially a reformulation of the notion proposed by Tschantz et al. The second one is a more liberal variant, relaxing the relation between them by integrating the notion of amortisation, which results into a more parsimonious use of the privacy budget. We show that the metrical closeness of automata guarantees the preservation of differential privacy, which makes the two metrics suitable for verification. Moreover we show that process combinators are non-expansive in this pseudometric framework. We apply the pseudometric framework to reason about the degree of differential privacy of protocols by the example of the Dining Cryptographers Protocol with biased coins. © 2014 IFIP International Federation for Information Processing.; Originally proposed for privacy protection in the context of statistical databases, differential privacy is now widely adopted in various models of computation. In this paper we investigate techniques for proving differential privacy in the context of concurrent systems. Our motivation stems from the work of Tschantz et al., who proposed a verification method based on proving the existence of a stratified family between states, that can track the privacy leakage, ensuring that it does not exceed a given leakage budget. We improve this technique by investigating a state property which is more permissive and still implies differential privacy. We consider two pseudometrics on probabilistic automata: The first one is essentially a reformulation of the notion proposed by Tschantz et al. The second one is a more liberal variant, relaxing the relation between them by integrating the notion of amortisation, which results into a more parsimonious use of the privacy budget. We show that the metrical closeness of automata guarantees the preservation of differential privacy, which makes the two metrics suitable for verification. Moreover we show that process combinators are non-expansive in this pseudometric framework. We apply the pseudometric framework to reason about the degree of differential privacy of protocols by the example of the Dining Cryptographers Protocol with biased coins. © 2014 IFIP International Federation for Information Processing.
Language英语
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/16603
Collection中国科学院软件研究所
Recommended Citation
GB/T 7714
Xu, Lili ,Chatzikokolakis, Konstantinos ,Lin, Huimin . Metrics for differential privacy in concurrent systems[C]. Springer Verlag,2014:199-215.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xu, Lili (1)]'s Articles
[Chatzikokolakis, Konstantinos (2)]'s Articles
[Lin, Huimin (4)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xu, Lili (1)]'s Articles
[Chatzikokolakis, Konstantinos (2)]'s Articles
[Lin, Huimin (4)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xu, Lili (1)]'s Articles
[Chatzikokolakis, Konstantinos (2)]'s Articles
[Lin, Huimin (4)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.