<i>L</i>
            -diversity

Machanavajjhala, Ashwin; Kifer, Daniel; Gehrke, Johannes; Venkitasubramaniam, Muthuramakrishnan

doi:10.1145/1217299.1217302

Cited by 2,388 publications

(225 citation statements)

References 47 publications

Supporting

Mentioning

220

Contrasting

Unclassified

Order By: Relevance

“…The corresponding decision problem kAnonymity asks, additionally given an upper bound s for the number of suppressions allowed, whether a matrix can be made k-anonymous by suppressing (blanking out) at most s entries. While k-Anonymity is our central data anonymization problem, our results also extend to several more restrictive anonymization problems, 1 including p-Sensitivity (Truta and Vinay, 2006) and -Diversity (Machanavajjhala et al, 2007).…”

Section: Introductionmentioning

confidence: 63%

The effect of homogeneity on the computational complexity of combinatorial data anonymization

Bredereck

Nichterlein

Niedermeier

et al. 2012

Data Min Knowl Disc

View full text Add to dashboard Cite

A matrix M is said to be k-anonymous if for each row r in M there are at least k − 1 other rows in M which are identical to r. The NP-hard k-Anonymity problem asks, given an n × m-matrix M over a fixed alphabet and an integer s > 0, whether M can be made k-anonymous by suppressing (blanking out) at most s entries. Complementing previous work, we introduce two new "data-driven" parameterizations for k-Anonymity-the number t in of different input rows and the number t out of different output rowsboth modeling aspects of data homogeneity. We show that k-Anonymity is fixed-parameter tractable for the parameter t in , and that it is NP-hard even for t out = 2 and alphabet size four. Notably, our fixed-parameter tractability result implies that k-Anonymity can be solved in linear time when t in is a constant. Our computational hardness results also extend to the related privacy problems p-Sensitivity and -Diversity, while our fixed-parameter tractability results extend to p-Sensitivity and the usage of domain generalization hierarchies, where the entries are replaced by more general data instead of being completely suppressed.Keywords k-Anonymity · p-Sensitivity · -Diversity · Domain generalization hierarchies · Matrix modification problems · Parameterized algorithmics · Fixed-parameter tractability · NP-hardness An extended abstract entitled "The Effect of Homogeneity on the Complexity of kAnonymity" appeared in

show abstract

Section: Introductionmentioning

confidence: 63%

The effect of homogeneity on the computational complexity of combinatorial data anonymization

Bredereck

Nichterlein

Niedermeier

et al. 2012

Data Min Knowl Disc

View full text Add to dashboard Cite

show abstract

“…To protect the re-identification of identity, k-anonymity [16] and l-diversity [17], among other methods, have been proposed to publish relational data with privacy preservation. Both k-anonymity and l-diversity use perturbation, suppression, and generalization to anonymize data.…”

Section: Anonymization Techniquesmentioning

confidence: 99%

An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

Hasan

et al. 2018

IJGI

View full text Add to dashboard Cite

How can training performance data (e.g., running or walking routes) be collected, measured, and published in a mobile program while preserving user privacy? This question is becoming important in the context of the growing use of reward-based location-based service (LBS) applications, which aim to promote employee training activities and to share such data with insurance companies in order to reduce the healthcare insurance costs of an organization. One of the main concerns of such applications is the privacy of user trajectories, because the applications normally collect user locations over time with identities. The leak of the identified trajectories often results in personal privacy breaches. For instance, a trajectory would expose user interest in places and behaviors in time by inference and linking attacks. This information can be used for spam advertisements or individual-based assaults. To the best of our knowledge, no existing studies can be directly applied to solve the problem while keeping data utility. In this paper, we identify the personal privacy problem in a reward-based LBS application and propose privacy architecture with a bounded perturbation technique to protect user's trajectory from the privacy breaches. Bounded perturbation uses global location set (GLS) to anonymize the trajectory data. In addition, the bounded perturbation will not generate any visiting points that are not possible to visit in real time. The experimental results on real-world datasets demonstrate that the proposed bounded perturbation can effectively anonymize location information while preserving data utility compared to the existing methods.

show abstract

“…A more flexible approach is proposed [15] by relaxing the constraint, where every value in the generalization domain is considered identical. Numerous methods including t-closeness, L-diversity, and δ-presence are introduced [16], [17], [11] to achieve the privacy.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid K-anonymity Data Relocation Technique for Privacy Preserved Data Mining in Cloud Computing

Aldeen¹,

Salleh²

2016

Journal of Internet Computing and Services

View full text Add to dashboard Cite

The unprecedented power of cloud computing (CC) that enables free sharing of confidential data records for further analysis and mining has prompted various security threats. Thus, supreme cyberspace security and mitigation against adversaries attack during data mining became inevitable. So, privacy preserving data mining is emerged as a precise and efficient solution, where various algorithms are developed to anonymize the data to be mined. Despite the wide use of generalized K-anonymizing approach its protection and truthfulness potency remains limited to tiny output space with unacceptable utility loss. By combining L-diversity and (α,k)-anonymity, we proposed a hybrid K-anonymity data relocation algorithm to surmount such limitation. The data relocation being a tradeoff between trustfulness and utility acted as a control input parameter. The performance of each K-anonymity's iteration is measured for data relocation. Data rows are changed into small groups of indistinguishable tuples to create anonymizations of finer granularity with assured privacy standard. Experimental results demonstrated considerable utility enhancement for relatively small number of group relocations.

show abstract

L -diversity

Cited by 2,388 publications

References 47 publications

The effect of homogeneity on the computational complexity of combinatorial data anonymization

The effect of homogeneity on the computational complexity of combinatorial data anonymization

An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

A Hybrid K-anonymity Data Relocation Technique for Privacy Preserved Data Mining in Cloud Computing

Contact Info

Product

Resources

About