Improved Generalization for Secure Data Publishing

Yaseen, Saba; Abbas, Syed Minhal; Anjum, Adeel; Saba, Tanzila; Khan, Abid; Malik, Saif Ur Rehman; Ahmad, Naveed; Shahzad, Basit

doi:10.1109/access.2018.2828398

Cited by 41 publications

(35 citation statements)

References 27 publications

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“…Consider agriculture application using WSN. [83][84][85][86][87][88][89][90][91][92] The sensors used in this application include soil moisture detection sensors, location sensors, humidity detection sensors, temperature sensors, optical sensors, electrochemical sensors, airflow sensors, and so on. If the sensors are collecting the temperature in a tropical country like African countries, most of the times, the temperature will be on a higher side.…”

Section: Imbalanced Data In Wsnsmentioning

confidence: 99%

A review on classification of imbalanced data for wireless sensor networks

Patel

Rajput

Reddy

et al. 2020

International Journal of Distributed Sensor Networks

121

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Classification of imbalanced data is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers, that is, classifiers become biased toward the class having larger amount of data. The data generated from wireless sensor networks will have several imbalances. This review article is a decent analysis of imbalance issue for wireless sensor networks and other application domains, which will help the community to understand WHAT, WHY, and WHEN of imbalance in data and its remedies.

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Section: Imbalanced Data In Wsnsmentioning

confidence: 99%

A review on classification of imbalanced data for wireless sensor networks

Patel

Rajput

Reddy

et al. 2020

International Journal of Distributed Sensor Networks

121

View full text Add to dashboard Cite

show abstract

“…• Full-domain generalization: This technique emphasizes on the level that each value of an attribute is located in the generalization tree. If a value of an attribute is generalized to its parent node, all values of that attribute in the dataset must be generalized to the same level [14]- [16].…”

Section: Generalization Techniquementioning

confidence: 99%

Privacy Preserving Location Data Publishing: A Machine Learning Approach

Shaham

Ding

Liu

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to TDrive and Geolife location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.

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“…In [126] values are replaced following a predefined generalisation hierarchy in which original values are substituted with a more generic representation of their domain.…”

Section: A Generalisationmentioning

confidence: 99%

“…More flexible operators offer a greater solution space but the search for a solution might be significantly harder. The importance of selecting the right DGH has been studied in [126], [136] B. SUPPRESSION Suppression is the erasure of specific values from the original dataset. Different levels of suppression can be considered.…”

Section: A Generalisationmentioning

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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Improved Generalization for Secure Data Publishing

Cited by 41 publications

References 27 publications

A review on classification of imbalanced data for wireless sensor networks

A review on classification of imbalanced data for wireless sensor networks

Privacy Preserving Location Data Publishing: A Machine Learning Approach

A Survey on Privacy Properties for Data Publishing of Relational Data

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