An incremental privacy-preservation algorithm for the (k, e)-Anonymous model

Srisungsittisunti, Bowonsak; Natwichai, Juggapong

doi:10.1016/j.compeleceng.2014.10.007

Cited by 10 publications

(9 citation statements)

References 27 publications

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“…However, in such cases the incremental privacy risk that arises when different anonymised versions of the same dataset are released, has to be also taken into consideration. While several approaches to this challenge can be found in the literature [74]- [76], [243], yet there is still a big gap to be filled. Furthermore, it has been shown that machine learning algorithms can be exploited to extract further knowledge from the anonymised dataset [244].…”

Section: Open Questions and Future Directionsmentioning

confidence: 99%

A Survey on Privacy Properties for Data Publishing of Relational Data

et al. 2020

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Recent advances in telecommunications and database systems have allowed the scientific community to efficiently mine vast amounts of information worldwide and to extract new knowledge by discovering hidden patterns and correlations. Nevertheless, all this shared information can be used to invade the privacy of individuals through the use of fusion and mining techniques. Simply removing direct identifiers such as name, SSN, or phone number is not anymore sufficient to prevent against these practices. In numerous cases, other fields, like gender, date of birth and/or zipcode, can be used to re-identify individuals and to expose their sensitive details, e.g. their medical conditions, financial statuses and transactions, or even their private connections. The scope of this work is to provide an in-depth overview of the current state of the art in Privacy-Preserving Data Publishing (PPDP) for relational data. To counter information leakage, a number of data anonymisation methods have been proposed during the past few years, including k-anonymity,-diversity, t-closeness, to name a few. In this study we analyse these methods providing concrete examples not only to explain how each of them works, but also to facilitate the reader to understand the different usage scenarios in which each of them can be applied. Furthermore, we detail several attacks along with their possible countermeasures, and we discuss open questions and future research directions.

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Section: Open Questions and Future Directionsmentioning

confidence: 99%

A Survey on Privacy Properties for Data Publishing of Relational Data

et al. 2020

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“…They introduced m-invariance to have the bound of the inferring confidence and added fake tuples to reduce information loss. In [15], a polynomial-time algorithm based on the (k, e)-anonymity model was designed to solve the problem of an incremental privacy breach. Zhang et al [13] proposed an efficient QI index-based approach for privacy preservation over incremental data sets on cloud.…”

Section: Of 18mentioning

confidence: 99%

“…The models mentioned earlier assume only a single data set release, and they are not applicable for incremental data. Many anonymization techniques are presented to solve the new type of privacy problem caused by the potential linkage between different data releases . Xiao and Tao discussed the more general case that includes tuple insertions and deletions.…”

Section: Related Workmentioning

confidence: 99%

Personalized extended (α, k)‐anonymity model for privacy‐preserving data publishing

Liu

Xie

Wang

2016

Concurrency and Computation

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General (˛, k)-anonymity model is a widely used method in privacy-preserving data publishing, but it cannot provide personalized anonymity. At present, two main schemes for personalized anonymity are the individual-oriented anonymity and the sensitive value-oriented anonymity. Unfortunately, the existing personalized anonymity models, designed for any of the aforementioned schemes for privacy-preserving data publishing, are not effective enough to meet the personalized privacy preservation requirement. In this paper, we propose a novel personalized extended scheme to provide the personalized services in general (˛, k)-anonymity model. The sensitive value-oriented anonymity is combined with the individual-oriented anonymity in the new personalized extended (˛, k)-anonymity model by the following two steps: (1) The sensitive attribute values are divided into several groups according to their sensitivities, and each group is assigned with its own frequency constraint threshold. (2) A guarding node is set for each individual to replace his/her sensitive value if necessary. We implement the personalized extended (˛, k)-anonymity model with a clustering algorithm. The performance evaluation finally shows that our model can provide stronger privacy preservation efficiently as well as achieving the personalized service. k-anonymity model assumes that each particular individual corresponds to a record in a twodimensional data set. Each record contains three types of attributes: identifier, quasi-identifier (QI), and sensitive attribute. Identifier is removed first; QI values are replaced with values that lessspecific but semantically consistent. As a result, each record in the modified data set is identical to at least k -1 other records with respect to QI. Based on k-anonymity model, a variety of improved privacy principles, such as l-diversity model [2], t-closeness model [3], general (˛, k)-anonymity model [4], are put forth to model and quantify privacy.The traditional anonymization principles mentioned earlier, however, focus on a universal approach that exerts the same amount of preservation for all individuals and do not take the personalized privacy preservation requirement into account. Individuals may have many differences in the sensitivity of the same privacy information in real life, which would lead to different privacy preservation requirements. Therefore, it is necessary to provide the privacy preservation of personalized service. The notion of personalized anonymity [5] was first proposed by Xiao et al., and some personalized anonymization solutions were subsequently given out [6][7][8][9][10]. In general, these personalized anonymization principles include two kinds of schemes, individual-oriented personalized anonymity and sensitive value-oriented personalized anonymity. The former may result in the impropriety of some parameters relevant to privacy constraint if complying with the privacy preservation requirements of individuals only. The latter ignores the privacy preservation requirements of individu...

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“…Experimental model on comparisons and benchmark datasets with other diversity dependent process and various conventional techniques were assuring. Srisungsittisunti et al [31] proposed the crisis of preserving data based on a prominent security type. Moreover, obtained results demonstrate the effectiveness of the algorithm on practical significance were accessed.…”

Section: A State-of-the-art Of Contributionsmentioning

confidence: 99%

Route Map of Privacy Preservation to IOT

Kathavate¹,

Amudhavel²

2018

IJET

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Data anonymization is the main feature of privacy preservation, and it assists in eradicating the privacy hazard in data preparation in various applications including IoT. Pseudonymity and Anonymization are two significant security factors that were adopted when sensitive data are shared. In medical field, data is usually distributed horizontally with diverse regions carrying a similar set of characteristics for various anonymization techniques.

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An incremental privacy-preservation algorithm for the (k, e)-Anonymous model

Cited by 10 publications

References 27 publications

A Survey on Privacy Properties for Data Publishing of Relational Data

A Survey on Privacy Properties for Data Publishing of Relational Data

Personalized extended (α, k)‐anonymity model for privacy‐preserving data publishing

Route Map of Privacy Preservation to IOT

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