Personalized privacy preservation

Xiao, Xiaokui; Tao, Yufei

doi:10.1145/1142473.1142500

Cited by 553 publications

(311 citation statements)

References 25 publications

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“…This solution is based on the intuition that each private data is associated to a given sensitivity level, which depends on the precision of the data itself; generally, the lesser the data is precise, the lesser it is sensitive. Obfuscation techniques have been applied to the protection of microdata released from databases (e.g., [35]). …”

Section: Obfuscation Of Context Datamentioning

confidence: 99%

Privacy in Georeferenced Context-Aware Services: A Survey

Riboni

Pareschi

Bettini

2009

Privacy in Location-Based Applications

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Abstract. Location based services (LBS) are a specific instance of a broader class of Internet services that are predicted to become popular in a near future: context-aware services. The privacy concerns that LBS have raised are likely to become even more serious when several context data, other than location and time, are sent to service providers as part of an Internet request. This paper provides a classification and a brief survey of the privacy preservation techniques that have been proposed for this type of services. After identifying the benefits and shortcomings of each class of techniques, the paper proposes a combined approach to achieve a more comprehensive solution for privacy preservation in georeferenced context-aware services.

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Section: Obfuscation Of Context Datamentioning

confidence: 99%

Privacy in Georeferenced Context-Aware Services: A Survey

Riboni

Pareschi

Bettini

2009

Privacy in Location-Based Applications

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“…Clearly, ignoring it may lead to offering insufficient protection to a subset of people while applying excessive protection to the privacy of another subset. It is worth pointing out that we do not draw a distinction between sensitive attributes and quasi-identifiers [13], [24], [12]. Rather, our framework provides more flexibility by enabling the owners of the data to supply the sensitivity of their attributes at their discretion.…”

Section: Privacy Risk Frameworkmentioning

confidence: 99%

Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk

Lebanon

Scannapieco

Fouad

et al. 2006

Privacy in Statistical Databases

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An important issue any organization or individual has to face when managing data containing sensitive information, is the risk that can be incurred when releasing such data. Even though data may be sanitized before being released, it is still possible for an adversary to reconstruct the original data using additional information thus resulting in privacy violations. To date, however, a systematic approach to quantify such risks is not available. In this paper we develop a framework, based on statistical decision theory, that assesses the relationship between the disclosed data and the resulting privacy risk. We model the problem of deciding which data to disclose, in terms of deciding which disclosure rule to apply to a database. We assess the privacy risk by taking into account both the entity identification and the sensitivity of the disclosed information. Furthermore, we prove that, under some conditions, the estimated privacy risk is an upper bound on the true privacy risk. Finally, we relate our framework with the k-anonymity disclosure method. The proposed framework makes the assumptions behind k-anonymity explicit, quantifies them, and extends them in several natural directions.

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“…Maximizing the utility u is analogous to minimizing the information loss △u and, therefore, it is straightforward to transfer the optimization problem from one of these utility measures to the other. Xiai and Tao [17] defined the information loss as follows: △u(a) = k i=1 (n i − 1)/m i , where m i and n i are defined as above. Likewise, Iyengar [4] proposes the LM loss metric which is based on summing up normalized information losses for each attribute i.e.…”

Section: A Utility Assessment Modelsmentioning

confidence: 99%

“…A personalized generalization technique is proposed by Xiao and Tao [17]. Under such approach users define maximum allowable specialization levels for their different attributes.…”

Section: Related Workmentioning

confidence: 99%

“…Many models have been proposed to quantify data utility all of which show that data generalization has negative impact on how useful data is. Xiao et al [17] define the information loss of a more general attribute value v * in terms of the number of values that it represents. Under the approach by Bayardo and Agrawal [1] and Xu et al [18], a penalty cost is assigned to a generalized or suppressed tuple to reflect the information loss in such transformations.…”

Section: Introductionmentioning

confidence: 99%

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ARUBA: A Risk-Utility-Based Algorithm for Data Disclosure

Fouad

Lebanon

Bertino

Lecture Notes in Computer Science

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Dealing with sensitive data has been the focus of much of recent research. On one hand data disclosure may incur some risk due to security breaches, but on the other hand data sharing has many advantages. For example, revealing customer transactions at a grocery store may be beneficial when studying purchasing patterns and market demand. However, a potential misuse of the revealed information may be harmful due to privacy violations. In this paper we study the tradeoff between data disclosure and data retention. Specifically, we address the problem of minimizing the risk of data disclosure while maintaining its utility above a certain acceptable threshold. We formulate the problem as a discrete optimization problem and leverage the special monotonicity characteristics for both risk and utility to construct an efficient algorithm to solve it. Such an algorithm determines the optimal transformations that need to be performed on the microdata before it gets released. These optimal transformations take into account both the risk associated with data disclosure and the benefit of it (referred to as utility). Through extensive experimental studies we compare the performance of our proposed algorithm with other date disclosure algorithms in the literature in terms of risk, utility, and time. We show that our proposed framework outperforms other techniques for sensitive data disclosure.

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Personalized privacy preservation

Cited by 553 publications

References 25 publications

Privacy in Georeferenced Context-Aware Services: A Survey

Privacy in Georeferenced Context-Aware Services: A Survey

Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk

ARUBA: A Risk-Utility-Based Algorithm for Data Disclosure

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