Ant-driven clustering for utility-aware disassociation of set-valued datasets

Awad, Nancy; Couchot, Jean-François; Bouna, Béchara Al; Philippe, Laurent

doi:10.1145/3331076.3331084

Cited by 5 publications

(12 citation statements)

References 18 publications

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“…Set-valued data is common in logs of search engines, search by hashtags, and can be also found in databases such as health databases and market basket records. This work is an extension of [1] which studies the privacy-utility trade-off of certain predefined associations in an anonymized set-valued data through the disassociation technique, defined first in [2]. The aim of this paper is to continue the investigation and reconstruct the disassociated set-valued dataset to be considerate to future data analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Guided by the disassociation technique our investigation started in [1] by studying and improving the probabilistic preservation of associations in a disassociated dataset. After examination of the probabilistic preservation results, we present an optimization of disassociation for a predefined set of associations, we call utility rules.…”

Section: Introductionmentioning

confidence: 99%

“…The new problematic in this extension of the work [1], is that in reality, we cannot define every association present in the original dataset as a utility rule in order to optimize its preservation for future analysis. Therefore, the study of association rules in a disassociated dataset cannot be limited to items preserved together without a break up after vertical partitioning.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, the "neighbor datasets" terminology seems to fall perfectly to our intention. In an effort to make this article self-sufficient, Section 2 recalls and summarizes our previous work [1] for probabilistic preservation of utility in a disassociated dataset and its implementation as ant clustering. In Section 3, we present our first contribution where we define "neighbor datasets" as two similar datasets that fall under a certain radius of distance and present a way to asses the distance between the "neighbor datasets" by formalizing the datasets into trees.…”

Section: Introductionmentioning

confidence: 99%

“…In [1] the utility of an association in disassociated datasets is evaluated theoretically under the k m -anonymity privacy model, and is proven to be:…”

mentioning

confidence: 99%

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Publishing Anonymized Set-Valued Data via Disassociation towards Analysis

et al. 2020

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Data publishing is a challenging task for privacy preservation constraints. To ensure privacy, many anonymization techniques have been proposed. They differ in terms of the mathematical properties they verify and in terms of the functional objectives expected. Disassociation is one of the techniques that aim at anonymizing of set-valued datasets (e.g., discrete locations, search and shopping items) while guaranteeing the confidentiality property known as k m -anonymity. Disassociation separates the items of an itemset in vertical chunks to create ambiguity in the original associations. In a previous work, we defined a new ant-based clustering algorithm for the disassociation technique to preserve some items associated together, called utility rules, throughout the anonymization process, for accurate analysis. In this paper, we examine the disassociated dataset in terms of knowledge extraction. To make data analysis easy on top of the anonymized dataset, we define neighbor datasets or in other terms datasets that are the result of a probabilistic re-association process. To assess the neighborhood notion set-valued datasets are formalized into trees and a tree edit distance (TED) is directly applied between these neighbors. Finally, we prove the faithfulness of the neighbors to knowledge extraction for future analysis, in the experiments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In [1] the utility of an association in disassociated datasets is evaluated theoretically under the k m -anonymity privacy model, and is proven to be:…”

mentioning

confidence: 99%

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Publishing Anonymized Set-Valued Data via Disassociation towards Analysis

et al. 2020

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Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

2022

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With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user's privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.

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A New Approach for Anonymizing Transaction Data with Set Values

Kim

2023

Electronics

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This article proposes a new method that can guarantee strong privacy while minimizing information loss in transactional data composed of a set of each attribute value in a relational database, which is not generally well-known structured data. The proposed scheme adopts the same top-down partitioning algorithm as the existing k-anonymity model, using local generalization to optimize safety and CPU execution time. At the same time, the information loss rate, which is a disadvantage of the existing local generalization, is further improved by reallocating transactions through an additional bottom-up tree search process after the partitioning process. Our scheme shows a very fast processing time compared to the HgHs algorithm using generalization and deletion techniques. In terms of information loss, our scheme shows much better performance than any schemes proposed so far, such as the existing local generalization or HgHs algorithm. In order to evaluate the efficiency of our algorithm, the experiment compared its performance with the existing local generalization and the HgHs algorithm, in terms of both execution time and information loss rate. As a result of the experiment, for example, when k is 5 in k-anonymity for the dataset BMS-WebView-2, the execution time of our scheme is up to 255 times faster than the HgHs algorithm, and with regard to the information loss rate, our method showed a maximum rate of 62.37 times lower than the local generalization algorithm.

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Ant-driven clustering for utility-aware disassociation of set-valued datasets

Cited by 5 publications

References 18 publications

Publishing Anonymized Set-Valued Data via Disassociation towards Analysis

Publishing Anonymized Set-Valued Data via Disassociation towards Analysis

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

A New Approach for Anonymizing Transaction Data with Set Values

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