Anonymizing Tables

Aggarwal, Gagan; Feder, Tomás; Kenthapadi, Krishnaram; Motwani, Rajeev; Panigrahy, Rina; Thomas, Dilys; An, Zhijie

doi:10.1007/978-3-540-30570-5_17

Cited by 203 publications

(224 citation statements)

References 13 publications

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“…However, most approach for preserving privacy using k-anonymity in recently [8]- [10]. Data displaying through k-anonymity by generalized.…”

Section: Data Encryptionmentioning

confidence: 99%

Preserving Privacy in Outsourced Database

Lin¹,

Yang²,

Lin³

et al. 2014

IJCCE

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Abstract-The data rapidly increases with time. To deal massive data, or called big data, the idea of database as a service has been proposed. Outsourced database provider offers a lot of computing power and storage area. For organizations, they do not need to build their own infrastructure. It can reduce the cost of consumption. However, the data stored in the third-party provider side, if the third party can not be trusted, sensitive data within organization will have leaked crisis. In order to preserve privacy in organization's database, a number of approaches for preserving privacy have been proposed. Although the database has many security problems should be addressed, such as authentication, integrity, access control, and privacy. Also, studies on database need to consider performance efficiently when data updating. But there are huge problems so that we can not address easily. Hence, this paper mainly discusses the recently proposed approaches for preserving privacy only. We classify and organize approaches, and also discuss these. Approaches are divided into two categories, data encryption and data fragmentation. We introduce in simple common approach, give an illustration, and finally discuss challenges for each approach respectively. In the approach of data encryption, we introduce k-anonymity which is a mainstream solution. In data fragmentation, we introduce clustering which is new idea solution. Finally, we summarize these approaches for preserving privacy and discuss research future works.Index Terms-Preserving privacy, outsourced database, database security.

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“…However, most approach for preserving privacy using k-anonymity in recently [8]- [10]. Data displaying through k-anonymity by generalized.…”

Section: Data Encryptionmentioning

confidence: 99%

Preserving Privacy in Outsourced Database

Lin¹,

Yang²,

Lin³

et al. 2014

IJCCE

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“…Our solution adapts for 2-microaggregation a corrected version of the [1] and [2] approach to 2-anonymizing categorical data through partial suppression. The algorithm in [1] and [2] could not be used as published, as it relies on [4] to solve a minimum weight [1,2]-factor, a problem not dealt with by [4], but by the substantially more recent literature mentioned in Section 2.…”

Section: A 2-approximation Algorithm For 2-microaggregationmentioning

confidence: 99%

“…2. Compute the minimum weight [1,2]-factor F min of graph G (see Section 2). By optimality, F min consists only of connected components with a single edge (two vertices) or two adjacent edges (three vertices).…”

Section: A 2-approximation Algorithm For 2-microaggregationmentioning

confidence: 99%

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Optimal Multivariate 2-Microaggregation for Microdata Protection: A 2-Approximation

Domingo‐Ferrer

Sebé

2006

Privacy in Statistical Databases

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Abstract. Microaggregation is a special clustering problem where the goal is to cluster a set of points into groups of at least k points in such a way that groups are as homogeneous as possible. Microaggregation arises in connection with anonymization of statistical databases for privacy protection (k-anonymity), where points are assimilated to database records. A usual group homogeneity criterion is within-groups sum of squares minimization SSE. For multivariate points, optimal microaggregation, i.e. with minimum SSE, has been shown to be NP-hard. Recently, a polynomial-time O(k 3 )-approximation heuristic has been proposed (previous heuristics in the literature offered no approximation bounds). The special case k = 2 (2-microaggregation) is interesting in privacy protection scenarios with neither internal intruders nor outliers, because information loss is lower: smaller groups imply smaller information loss. For 2-microaggregation the existing general approximation can only guarantee a 36-approximation. We give here a new polynomial-time heuristic whose SSE is at most twice the minimum SSE (2-approximation).

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“…Fung et al [5] present a top-down heuristic to make a table to be released k-anonymous. As to the theoretical results, Meyerson and Williams [9] and Aggarwal et al [1,2] proved the optimal k-anonymity is NP-hard (based on the number of cells and number of attributes that are generalized and suppressed) and describe approximation algorithms for optimal k-anonymity. Sun et al [13] proved that k-anonymity problem is also NP-hard even in the restricted cases, which could imply the results in [1,2,9] as well.…”

Section: Introductionmentioning

confidence: 99%

L-Diversity Based Dynamic Update for Large Time-Evolving Microdata

Sun

Wang

2008

AI 2008: Advances in Artificial Intelligence

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Abstract. Data anonymization techniques based on enhanced privacy principles have been the focus of intense research in the last few years. All existing methods achieving privacy principles assume implicitly that the data objects to be anonymized are given once and fixed, which makes it unsuitable for time evolving data. However, in many applications, the real world data sources are dynamic. In such dynamic environments, the current techniques may suffer from poor data quality and/or vulnerability to inference. In this paper, we investigate the problem of updating large time-evolving microdata based on the sophisticated l-diversity model, in which it requires that every group of indistinguishable records contains at least l distinct sensitive attribute values; thereby the risk of attribute disclosure is kept under 1/l. We analyze how to maintain the ldiversity against time evolving updating. The experimental results show that the updating technique is very efficient in terms of effectiveness and data quality.

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Anonymizing Tables

Cited by 203 publications

References 13 publications

Preserving Privacy in Outsourced Database

Preserving Privacy in Outsourced Database

Optimal Multivariate 2-Microaggregation for Microdata Protection: A 2-Approximation

L-Diversity Based Dynamic Update for Large Time-Evolving Microdata

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