Approximate algorithms for K-anonymity

Park, Hyoung-Min; Shim, Kyuseok

doi:10.1145/1247480.1247490

Cited by 109 publications

(100 citation statements)

References 16 publications

(34 reference statements)

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“…The actual objective is to maximize utility by minimizing the amount of generalization and suppression [94]. Achieving k-anonymity by generalization with this objective as a constraint is a Non-deterministic Polynomial-time hard (NP-hard) problem [95].…”

Section: K-anonymitymentioning

confidence: 99%

Privacy challenges in Ambient Intelligence systems

Caire

Moawad

Efthymiou

et al. 2016

AIS

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Abstract. Today, privacy is a key concept. It is also one which is rapidly evolving with technological advances, and there is no consensus on a single definition for it. In fact, the concept of privacy has been defined in many different ways, ranging from the "right to be left alone" to being a "commodity" that can be bought and sold. In the same time, powerful Ambient Intelligence (AmI) systems are being developed, that deploy context-aware, personalised, adaptive and anticipatory services. In such systems personal data is vastly collected, stored, and distributed, making privacy preservation a critical issue. The humancentred focus of AmI systems has prompted the introduction of new kinds of technologies, e.g. Privacy Enhancing Technologies (PET), and methodologies, e.g. Privacy by Design (PbD), whereby privacy concerns are included in the design of the system. One particular application field, where privacy preservation is of critical importance is Ambient Assisted Living (AAL). Emerging from the continuous increase of the ageing population, AAL focuses on intelligent systems of assistance for a better, healthier and safer life in their living environment. In this paper, we first build on our previous work, in which we introduced a new tripartite categorisation of privacy as a right, an enabler, and a commodity. Second, we highlight the specific privacy issues raised in AAL. Third, we review and discuss current approaches for privacy preservation. Finally, drawing on lessons learned from AAL, we provide insights on the challenges and opportunities that lie ahead. Part of our methodology is a statistical analysis performed on the IEEE publications database. We illustrate our work with AAL scenarios elaborated in cooperation with the city of Luxembourg.

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Section: K-anonymitymentioning

confidence: 99%

Privacy challenges in Ambient Intelligence systems

Caire

Moawad

Efthymiou

et al. 2016

AIS

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“…For the latter, they proposed an approximate algorithm that minimizes the number of suppressed values; the approximation bound is O(k · logk). [2] improved this bound to O(k), while [17] further reduced it to O(log k). Several approaches limit the search space by considering only global recoding.…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving anonymization of set-valued data

2008

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In this paper we study the problem of protecting privacy in the publication of set-valued data. Consider a collection of transactional data that contains detailed information about items bought together by individuals. Even after removing all personal characteristics of the buyer, which can serve as links to his identity, the publication of such data is still subject to privacy attacks from adversaries who have partial knowledge about the set. Unlike most previous works, we do not distinguish data as sensitive and non-sensitive, but we consider them both as potential quasi-identifiers and potential sensitive data, depending on the point of view of the adversary. We define a new version of the k-anonymity guarantee, the k m -anonymity, to limit the effects of the data dimensionality and we propose efficient algorithms to transform the database. Our anonymization model relies on generalization instead of suppression, which is the most common practice in related works on such data. We develop an algorithm which finds the optimal solution, however, at a high cost which makes it inapplicable for large, realistic problems. Then, we propose two greedy heuristics, which scale much better and in most of the cases find a solution close to the optimal. The proposed algorithms are experimentally evaluated using real datasets.

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“…However several heuristics have been proposed to provide fast k-anonymization algorithms (Park & Shim, 2007). We do not aim in this paper at presenting a new heuristic for detecting composed identity attributes so we rely on existing ones.…”

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confidence: 99%

A Semantic Approach for Semi-Automatic Detection of Sensitive Data

Akoka

Comyn-Wattiau

Mouza

et al. 2014

Information Resources Management Journal

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We propose an innovative approach and its implementation as an expert system to achieve the semiautomatic detection of candidate attributes for scrambling sensitive data. Our approach is based on semantic rules that determine which concepts have to be scrambled, and on a linguistic component that retrieves the attributes that semantically correspond to these concepts. Because attributes cannot be considered independently from each other, we also address the challenging problem of the propagation of the scrambling process through the entire database. One main contribution of our approach is to provide a semi-automatic process for the detection of sensitive data. The underlying knowledge is made available through production rules, operationalizing the detection of the sensitive data. A validation of our approach using four different databases is provided.

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Approximate algorithms for K-anonymity

Cited by 109 publications

References 16 publications

Privacy challenges in Ambient Intelligence systems

Privacy challenges in Ambient Intelligence systems

Privacy-preserving anonymization of set-valued data

A Semantic Approach for Semi-Automatic Detection of Sensitive Data

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