The Experts below are selected from a list of 261 Experts worldwide ranked by ideXlab platform
Shuhui Yang - One of the best experts on this subject based on the ideXlab platform.
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preserving Privacy with probabilistic indistinguishability in weighted social networks
IEEE Transactions on Parallel and Distributed Systems, 2017Co-Authors: Qin Liu, Guojun Wang, Shuhui YangAbstract:The increasing popularity of social networks has inspired recent research to explore social graphs for marketing and data mining. As social networks often contain sensitive information about individuals, preserving Privacy when publishing social graphs becomes an important issue. In this paper, we consider the identity disclosure problem in releasing weighted social graphs. We identify weighted 1*-neighborhood attacks, which assume that an attacker has knowledge about not only a target's one-hop neighbors and connections between them (1-neighborhood graph), but also related node degrees and edge weights. With this information, an attacker may re-identify a target with high confidence, even if any node's 1-neighborhood graph is isomorphic with $k-1$ other nodes’ graphs. To counter this attack while preserving high utility of the published graph, we define a key Privacy Property, probabilistic indistinguishability, and propose a heuristic indistinguishable group anonymization (HIGA) scheme to anonymize a weighted social graph with such a Property. Extensive experiments on both real and synthetic data sets illustrate the effectiveness and efficiency of the proposed scheme.
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INFOCOM - Outsourcing Privacy-preserving social networks to a cloud
2013 Proceedings IEEE INFOCOM, 2013Co-Authors: Guojun Wang, Qin Liu, Shuhui YangAbstract:In the real world, companies would publish social networks to a third party, e.g., a cloud service provider, for marketing reasons. Preserving Privacy when publishing social network data becomes an important issue. In this paper, we identify a novel type of Privacy attack, termed 1*-neighborhood attack. We assume that an attacker has knowledge about the degrees of a target's one-hop neighbors, in addition to the target's 1-neighborhood graph, which consists of the one-hop neighbors of the target and the relationships among these neighbors. With this information, an attacker may re-identify the target from a k-anonymity social network with a probability higher than 1/k, where any node's 1-neighborhood graph is isomorphic with k - 1 other nodes' graphs. To resist the 1*-neighborhood attack, we define a key Privacy Property, probability indistinguishability, for an outsourced social network, and propose a heuristic indistinguishable group anonymization (HIGA) scheme to generate an anonymized social network with this Privacy Property. The empirical study indicates that the anonymized social networks can still be used to answer aggregate queries with high accuracy.
Guojun Wang - One of the best experts on this subject based on the ideXlab platform.
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preserving Privacy with probabilistic indistinguishability in weighted social networks
IEEE Transactions on Parallel and Distributed Systems, 2017Co-Authors: Qin Liu, Guojun Wang, Shuhui YangAbstract:The increasing popularity of social networks has inspired recent research to explore social graphs for marketing and data mining. As social networks often contain sensitive information about individuals, preserving Privacy when publishing social graphs becomes an important issue. In this paper, we consider the identity disclosure problem in releasing weighted social graphs. We identify weighted 1*-neighborhood attacks, which assume that an attacker has knowledge about not only a target's one-hop neighbors and connections between them (1-neighborhood graph), but also related node degrees and edge weights. With this information, an attacker may re-identify a target with high confidence, even if any node's 1-neighborhood graph is isomorphic with $k-1$ other nodes’ graphs. To counter this attack while preserving high utility of the published graph, we define a key Privacy Property, probabilistic indistinguishability, and propose a heuristic indistinguishable group anonymization (HIGA) scheme to anonymize a weighted social graph with such a Property. Extensive experiments on both real and synthetic data sets illustrate the effectiveness and efficiency of the proposed scheme.
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INFOCOM - Outsourcing Privacy-preserving social networks to a cloud
2013 Proceedings IEEE INFOCOM, 2013Co-Authors: Guojun Wang, Qin Liu, Shuhui YangAbstract:In the real world, companies would publish social networks to a third party, e.g., a cloud service provider, for marketing reasons. Preserving Privacy when publishing social network data becomes an important issue. In this paper, we identify a novel type of Privacy attack, termed 1*-neighborhood attack. We assume that an attacker has knowledge about the degrees of a target's one-hop neighbors, in addition to the target's 1-neighborhood graph, which consists of the one-hop neighbors of the target and the relationships among these neighbors. With this information, an attacker may re-identify the target from a k-anonymity social network with a probability higher than 1/k, where any node's 1-neighborhood graph is isomorphic with k - 1 other nodes' graphs. To resist the 1*-neighborhood attack, we define a key Privacy Property, probability indistinguishability, for an outsourced social network, and propose a heuristic indistinguishable group anonymization (HIGA) scheme to generate an anonymized social network with this Privacy Property. The empirical study indicates that the anonymized social networks can still be used to answer aggregate queries with high accuracy.
Qin Liu - One of the best experts on this subject based on the ideXlab platform.
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preserving Privacy with probabilistic indistinguishability in weighted social networks
IEEE Transactions on Parallel and Distributed Systems, 2017Co-Authors: Qin Liu, Guojun Wang, Shuhui YangAbstract:The increasing popularity of social networks has inspired recent research to explore social graphs for marketing and data mining. As social networks often contain sensitive information about individuals, preserving Privacy when publishing social graphs becomes an important issue. In this paper, we consider the identity disclosure problem in releasing weighted social graphs. We identify weighted 1*-neighborhood attacks, which assume that an attacker has knowledge about not only a target's one-hop neighbors and connections between them (1-neighborhood graph), but also related node degrees and edge weights. With this information, an attacker may re-identify a target with high confidence, even if any node's 1-neighborhood graph is isomorphic with $k-1$ other nodes’ graphs. To counter this attack while preserving high utility of the published graph, we define a key Privacy Property, probabilistic indistinguishability, and propose a heuristic indistinguishable group anonymization (HIGA) scheme to anonymize a weighted social graph with such a Property. Extensive experiments on both real and synthetic data sets illustrate the effectiveness and efficiency of the proposed scheme.
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INFOCOM - Outsourcing Privacy-preserving social networks to a cloud
2013 Proceedings IEEE INFOCOM, 2013Co-Authors: Guojun Wang, Qin Liu, Shuhui YangAbstract:In the real world, companies would publish social networks to a third party, e.g., a cloud service provider, for marketing reasons. Preserving Privacy when publishing social network data becomes an important issue. In this paper, we identify a novel type of Privacy attack, termed 1*-neighborhood attack. We assume that an attacker has knowledge about the degrees of a target's one-hop neighbors, in addition to the target's 1-neighborhood graph, which consists of the one-hop neighbors of the target and the relationships among these neighbors. With this information, an attacker may re-identify the target from a k-anonymity social network with a probability higher than 1/k, where any node's 1-neighborhood graph is isomorphic with k - 1 other nodes' graphs. To resist the 1*-neighborhood attack, we define a key Privacy Property, probability indistinguishability, for an outsourced social network, and propose a heuristic indistinguishable group anonymization (HIGA) scheme to generate an anonymized social network with this Privacy Property. The empirical study indicates that the anonymized social networks can still be used to answer aggregate queries with high accuracy.
Kazunori Komatani - One of the best experts on this subject based on the ideXlab platform.
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Clarifying Privacy, Property, and Power: Case Study on Value Conflict Between Communities
Proceedings of the IEEE, 2019Co-Authors: Arisa Ema, Hirotaka Osawa, Reina Saijo, Akinori Kubo, Takushi Otani, Hiromitsu Hattori, Naonori Akiya, Nobutsugu Kanzaki, Minao Kukita, Kazunori KomataniAbstract:This study analyzes the value conflict of a paper on fan fiction writing that used online fan fiction novels as a source to extract and filter sexual expressions from text. The boundaries of public and private information are ambiguous because users are not always aware of or have agreed to the fact that their content is to be used openly. The case was complicated by the fact that the use of these data by researchers violated an unconsciously infringed upon right of a vulnerable community with a weak legal position. This paper describes the debate on this topic among researchers from engineering and humanities fields on whether the purpose of the research was ethically acceptable; how the systems can be embedded in ethical values; and what ethical, legal, social, and educational lessons are appropriate for governance of artificial intelligence (AI). Our analysis aimed not only to clarify the abstract concept of Privacy but also to make changes to the submission guidelines for authors. We hope that our analysis contributes to the governance of ethical AIs and AI ethics on handling sensitive aspects of online activities.
Mark A. Lemley - One of the best experts on this subject based on the ideXlab platform.
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Privacy, Property, and Publicity
Michigan Law Review, 2019Co-Authors: Mark A. LemleyAbstract:Review of Jennifer E. Rothman's The Right of Publicity: Privacy Reimagined for a Public World.
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Privacy, Property, and Publicity
SSRN Electronic Journal, 2018Co-Authors: Mark A. LemleyAbstract:In Jennifer Rothman’s new book The Right of Publicity: Privacy Reimagined for a Public Age, she argues that we have wrongly reconceived the right of publicity as an intellectual Property (IP) right rather than as a Privacy-like right of “self-ownership,” and that in doing so we have let it grow unchecked in ways that serve no good purpose. She endorses returning to the historical core of the right of publicity as a Privacy right that primarily protects human dignity, and argues that doing so will enable us to limit the growth of the doctrine and apply the First Amendment to effectively protect speech threatened by the current, mutant right of publicity. Rothman’s book is a compelling read, and her explication of the history and how we got here is fascinating and largely persuasive. And I agree with her both about the problems with the current broad form of the right of publicity and about many of the specific doctrinal changes we should make to cut it back to a manageable size. But I think there is a disconnect between the history she has uncovered and the theoretical and legal framework she proposes. The history of the right of publicity as a Privacy rather than an IP right is not encouraging for those who would limit the scope of the right or apply robust First Amendment principles to counterbalance it. The right of Privacy that grew into the right of publicity was, from the start, capacious, unruly, poorly cabined, and intolerant of free speech. While dropping the idea of the right of publicity as IP might solve particular problems such as its transferability, it is unlikely to give us the limits she wants on the substantive scope of the right itself. To get there, we would need to challenge the nature of the use of one’s identity as a Privacy harm in and of itself. Ironically, understanding the right of publicity as a specific form of IP right—a trademark-like right against deception—may point the way towards a more reasonable doctrine.