On the complexity of optimal K-anonymity

Meyerson, Adam; Williams, Ryan

doi:10.1145/1055558.1055591

Cited by 645 publications

(478 citation statements)

References 11 publications

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“…By generalization, the quasi-identifier values are replaced with less specific ones (e.g., replace specific age with a range of ages), so that after generalization, the original dataset is partitioned into groups, with each group consisting of at least k tuples that are of the same generalized quasiidentifier values [4,[13][14] .…”

Section: Generalization-based Techniquesmentioning

confidence: 99%

Privacy-Preserving Data Sharing in Cloud Computing

Wang

2010

J. Comput. Sci. Technol.

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Storing and sharing databases in the cloud of computers raise serious concern of individual privacy. We consider two kinds of privacy risk: presence leakage, by which the attackers can explicitly identify individuals in (or not in) the database, and association leakage, by which the attackers can unambiguously associate individuals with sensitive information. However, the existing privacy-preserving data sharing techniques either fail to protect the presence privacy or incur considerable amounts of information loss. In this paper, we propose a novel technique, Ambiguity, to protect both presence privacy and association privacy with low information loss. We formally define the privacy model and quantify the privacy guarantee of Ambiguity against both presence leakage and association leakage. We prove both theoretically and empirically that the information loss of Ambiguity is always less than the classic generalization-based anonymization technique. We further propose an improved scheme, PriView, that can achieve better information loss than Ambiguity. We propose efficient algorithms to construct both Ambiguity and PriView schemes. Extensive experiments demonstrate the effectiveness and efficiency of both Ambiguity and PriView schemes.

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Section: Generalization-based Techniquesmentioning

confidence: 99%

Privacy-Preserving Data Sharing in Cloud Computing

Wang

2010

J. Comput. Sci. Technol.

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“…The complexity of this algorithm is O(k log k), where the constant in the big-O is less than 4. Although the runtime of this algorithm is exponential in k, its efficiency can be greatly enhanced as suggested by [12].…”

Section: Performance Assessmentmentioning

confidence: 99%

Privacy Preserving Data Mining Research: Current Status and Key Issues

Chu

Wang

et al. 2007

Computational Science – ICCS 2007

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Abstract. Recent advances in the Internet, in data mining, and in security technologies have gave rise to a new stream of research, known as privacy preserving data mining (PPDM). PPDM technologies allow us to extract relevant knowledge from a large amount of data, while hide sensitive data or information from disclosure. Several research questions have often being asked:(1) what kind of option available for privacy preserving? (2) Which method is more popular? (3) how to measure the performance of these algorithms? And (4) how effective of these algorithms in preserving privacy? To help answer these questions, we conduct an extensive review of 29 recent references from years 2000 to 2006 for analysis.

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“…A greedy algorithm aimed at guaranteeing that no address unknown by the adversary can be linked with an user with probability higher than a given threshold is proposed in [21]. The main problem with this approach is that dealing with all possible adversary's knowledge becomes harder than the original k-anonymity problem, which is already known to be NP-Hard [13]. There exist other suppression-based methods in the literature, e.g., [6].…”

Section: Introductionmentioning

confidence: 99%

The Fréchet/Manhattan Distance and the Trajectory Anonymisation Problem

Torres

Trujillo-Rasúa

2016

Data and Applications Security and Privacy XXX

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Abstract. Mobile communication has grown quickly in the last two decades. Connections can be wirelessly established from almost any habitable place in the earth, leading to a plethora of connection-based tracking mechanisms, such as GPS, GSM, RFID, etc. Trajectories representing the movement of people are consequently being gathered and analysed in a daily basis. However, a trajectory may contain sensitive and private information, which raises the problem of whether spatio-temporal data can be published in a private manner. In this article, we introduce a novel distance measure for trajectories that captures both aspect of the microaggregation process, namely clustering and obfuscation. Based on this distance measure we propose a trajectory anonymisation heuristic method ensuring that each trajectory is indistinguishable from k −1 other trajectories. The proposed distance measure is loosely based on the Fréchet distance, yet it can be computed efficiently in quadratic time complexity. Empirical studies on synthetic trajectories show that our anonymisation approach improves previous work in terms of utility without sacrificing privacy.

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On the complexity of optimal K-anonymity

Cited by 645 publications

References 11 publications

Privacy-Preserving Data Sharing in Cloud Computing

Privacy-Preserving Data Sharing in Cloud Computing

Privacy Preserving Data Mining Research: Current Status and Key Issues

The Fréchet/Manhattan Distance and the Trajectory Anonymisation Problem

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