Coprivacy: Towards a Theory of Sustainable Privacy

Domingo-Ferrer, Josep

doi:10.1007/978-3-642-15838-4_23

Cited by 27 publications

(23 citation statements)

References 15 publications

(18 reference statements)

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“…If the utility functions U in Definition 1 only considers privacy, co-utility becomes the co-privacy notion introduced in [5,6]; if utilities only consider security, we could speak of co-security; if they only consider functionality, co-utility becomes co-functionality. We can use these definitions to obtain a game-theoretic model for the emergency reporting protocol with the following notations: (1) P i is the witness of the emergency or wants to attack the system; (2) P j is a hop (another member of the group) contacted by the witness to forward the emergency report to the reporter.…”

Section: Co-utility Model For the Proposed Solutionmentioning

confidence: 99%

“…Our proposal stems from a game-theoretic design inspired by the co-privacy (co-utility) approach [5,6], which leads to a mechanism of rewards and punishments to encourage legitimate information and discourage false reporting. Unlike existing emergency management systems in which witnesses are required to reveal their identities or personal information to the emergency service provider, our system protects the witness's identity by using the concept of groups, in such a way that the witness is indistinguishable from other members of the group.…”

Section: Introductionmentioning

confidence: 99%

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Enabling Collaborative Privacy in User-Generated Emergency Reports

Qureshi

Rifà-Pous

Megías

2016

Privacy in Statistical Databases

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Abstract. Witnesses are of the utmost importance in emergency systems since they can trigger timely location-based status alerts. However, their collaboration with the authorities can get impaired for the fear of the people of being involved with someone, some place, or even with the same cause of the emergency. Anonymous reporting solutions can encourage the witnesses, but they also pose a threat of system collapse if the authority receives many fake reports. In this paper, we propose an emergency reporting system that ensures the anonymity of honest witnesses but is able to disclose the identity and punish the malicious ones. The system is designed over an online social network that facilitates the indistinguishability of the witness among a group of users. We use a game-theoretic approach based on the co-privacy (co-utility) principles to encourage the users of the network to participate in the protocol. We also use discernible ring signatures to provide the property of conditional anonymity. In addition, the system is designed to provide rewards to a witness and his/her group members in a privacy-preserving manner.

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Section: Co-utility Model For the Proposed Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enabling Collaborative Privacy in User-Generated Emergency Reports

Qureshi

Rifà-Pous

Megías

2016

Privacy in Statistical Databases

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Section: Need For Protection Need For Informationmentioning

confidence: 99%

“…In addition, every 2 years the UNESCO sponsors an international conference (i.e., Privacy in Statistical Databases) that gathers worldwide experts from different disciplines to discuss current issues in the field. Proceedings are published by Springer in the series Lecture Notes in Computer Science (Domingo-Ferrer, Prada et al 3 2002;Domingo-Ferrer & Franconi, 2006;Domingo-Ferrer & Magkos, 2010;Domingo-Ferrer & Saygin, 2008; DomingoFerrer & Torra, 2004;European Communities, 1998).More recently, the book by Duncan, Elliot, and SalazarGonzález (2011) provides a comprehensive understanding of the principles and practice of statistical confidentiality. Other recent technical reviews are those by B. Chen, Kifer, LeFevre, andMachanavajjhala (2009) andFung, Wang, Chen, andYu (2010) on the latest developments in the field of Privacypreserving data publishing, and by Fayyoumi and Oommen (2010) on statistical disclosure control and microaggregation techniques for secure statistical databases.…”

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confidence: 99%

Avoiding Disclosure of Individually Identifiable Health Information

et al. 2011

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Achieving data and information dissemination without harming anyone is a central task of any entity in charge of collecting data. In this article, the authors examine the literature on data and statistical confidentiality. Rather than comparing the theoretical properties of specific methods, they emphasize the main themes that emerge from the ongoing discussion among scientists regarding how best to achieve the appropriate balance between data protection, data utility, and data dissemination. They cover the literature on de-identification and reidentification methods with emphasis on health care data. The authors also discuss the benefits and limitations for the most common access methods. Although there is abundant theoretical and empirical research, their review reveals lack of consensus on fundamental questions for empirical practice: How to assess disclosure risk, how to choose among disclosure methods, how to assess reidentification risk, and how to measure utility loss.Keywords public use files, disclosure avoidance, reidentification, de-identification, data utility 2 SAGE Open inferential disclosure (i.e., information that can be inferred about a record in a data set with better accuracy). There is significant literature on each of these topics, which are beyond the scope of this article.Our article is divided into six sections, of which this "Introduction" is the first. The second section presents "The Policy and Academic Context" surrounding the discussion. The third section discusses the state of the art in "De-Identification Methods," while the fourth emphasizes the state of the art in "Reidentification Methods." The fifth section presents the conclusions from the literature on the different ways in which users may "Access" public data, stressing the trade-offs between (a) confidentiality and utility and (b) confidentiality and ease of access. The last section presents the "Conclusion." The Policy and Academic Context Historic PerspectiveConcerns about privacy and confidentiality in governmental efforts to collect and disseminate information are not new. As a review by Anderson and Seltzer (2009) suggests, "the roots of the modern concept of federal statistical confidentiality can be traced directly back to the late nineteenth century" (p. 8). Notwithstanding this history, the literature on statistical disclosure methods is fairly recent by modern standards (Dalenius, 1977, is considered the seminal paper). In 1975, the U.S. Federal Committee on Statistical Methodology (FCSM) was organized by the Office of Management and Budget (OMB) to investigate issues of data quality affecting federal statistics. As part of this effort, the Subcommittee on Disclosure Limitation Methodology, created within the FCSM, published its 1994 Statistical Policy Working Paper 22 (SPWP22). This paper, which was revised in 2005 by the Confidentiality and Data Access Committee (CDAC, 2005), sets good practice guidelines and recommendations for all agencies regarding confidentiality protection. Defining Confidentiality ...

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“…Domingo-Ferrer initiates epitome of cooperation in privacy and termed it as Co-Privacy [4,5]. However, CoV (cooperative value) is modeled, that estimates the cooperation between the tuples using Cooperative Game Theory and it is titled as cooperative privacy.…”

mentioning

confidence: 99%

Cooperative privacy game: a novel strategy for preserving privacy in data publishing

Kumari

Chakravarthy²

2016

Hum. Cent. Comput. Inf. Sci.

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Achieving data privacy before publishing has been becoming an extreme concern of researchers, individuals and service providers. A novel methodology, Cooperative Privacy Game (CoPG), has been proposed to achieve data privacy in which Cooperative Game Theory is used to achieve the privacy and is named as Cooperative Privacy (CoP). The core idea of CoP is to play the best strategy for a player to preserve his privacy by himself which in turn contributes to preserving other players privacy. CoP considers each tuple as a player and tuples form coalitions as described in the procedure. The main objective of the CoP is to obtain individuals (player) privacy as a goal that is rationally interested in other individuals’ (players) privacy. CoP is formally defined in terms of Nash equilibria, i.e., all the players are in their best coalition, to achieve k-anonymity. The cooperative values of the each tuple are measured using the characteristic function of the CoPG to identify the coalitions. As the underlying game is convex; the algorithm is efficient and yields high quality coalition formation with respect to intensity and disperse. The efficiency of anonymization process is calculated using information loss metric. The variations of the information loss with the parameters $$\alpha$$ α (weight factor of nearness) and $$\beta$$ β (multiplicity) are analyzed and the obtained results are discussed.

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Coprivacy: Towards a Theory of Sustainable Privacy

Cited by 27 publications

References 15 publications

Enabling Collaborative Privacy in User-Generated Emergency Reports

Enabling Collaborative Privacy in User-Generated Emergency Reports

Avoiding Disclosure of Individually Identifiable Health Information

Cooperative privacy game: a novel strategy for preserving privacy in data publishing

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