Microaggregation heuristic applied to statistical disclosure control

Fadel, Augusto; Ochi, Luiz Satoru; Brito, José André de Moura; Semaan, Gustavo Silva

doi:10.1016/j.ins.2020.09.069

Cited by 10 publications

(9 citation statements)

References 25 publications

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“…To assess the quality of the sanitation algorithms in terms of information utility and privacy risk, we use two standard metrics in the literature, namely Information Loss and Disclosure Risk [ 1 , 2 , 3 , 4 , 5 , 6 ]. In the following paragraphs, we define how both functions are implemented.…”

Section: Materials and Methodsmentioning

confidence: 99%

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Toward a Comparison of Classical and New Privacy Mechanism

Heredia-Ductram

Núñez-del-Prado

Alatrista-Salas

2021

Entropy

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In the last decades, the development of interconnectivity, pervasive systems, citizen sensors, and Big Data technologies allowed us to gather many data from different sources worldwide. This phenomenon has raised privacy concerns around the globe, compelling states to enforce data protection laws. In parallel, privacy-enhancing techniques have emerged to meet regulation requirements allowing companies and researchers to exploit individual data in a privacy-aware way. Thus, data curators need to find the most suitable algorithms to meet a required trade-off between utility and privacy. This crucial task could take a lot of time since there is a lack of benchmarks on privacy techniques. To fill this gap, we compare classical approaches of privacy techniques like Statistical Disclosure Control and Differential Privacy techniques to more recent techniques such as Generative Adversarial Networks and Machine Learning Copies using an entire commercial database in the current effort. The obtained results allow us to show the evolution of privacy techniques and depict new uses of the privacy-aware Machine Learning techniques.

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Section: Materials and Methodsmentioning

confidence: 99%

“…To compare the algorithms cited above, two measures widely used in the literature [ 1 , 2 , 3 , 4 , 5 , 6 ] were used, namely, Disclosure Risk and Information Loss. The former quantifies the danger of finding the same distribution for the output variable after a prediction task when the input dataset is sanitized.…”

Section: Introductionmentioning

confidence: 99%

Toward a Comparison of Classical and New Privacy Mechanism

Heredia-Ductram

Núñez-del-Prado

Alatrista-Salas

2021

Entropy

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“…Microaggregation is also very used for perturbation of microdata sets and has been enhanced in terms of disclosure risk. For instance, Fadel et al [45] presented very recently a heuristic approach to apply microaggregation that aims to reduce the disclosure risk when compared with other approaches. On the other hand, existing works in microaggregation shows that this technique either produce a low degree of with-in cluster homogeneity or fail to reduce the amount of noise independent of the size of a data set and for these reasons, Iftikhar et al [73] propose an interesting approach that uses microaggregation for generating differentially private data sets.…”

Section: Impact On Data Privacymentioning

confidence: 99%

Survey on Privacy-Preserving Techniques for Data Publishing

Carvalho¹,

Moniz²,

Faria³

et al. 2022

Preprint

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The exponential growth of collected, processed, and shared microdata has given rise to concerns about individuals' privacy. As a result, laws and regulations have emerged to control what organisations do with microdata and how they protect it. Statistical Disclosure Control seeks to reduce the risk of confidential information disclosure by de-identifying them. Such de-identification is guaranteed through privacy-preserving techniques. However, de-identified data usually results in loss of information, with a possible impact on data analysis precision and model predictive performance. The main goal is to protect the individuals' privacy while maintaining the interpretability of the data, i.e. its usefulness. Statistical Disclosure Control is an area that is expanding and needs to be explored since there is still no solution that guarantees optimal privacy and utility. This survey focuses on all steps of the de-identification process. We present existing privacy-preserving techniques used in microdata de-identification, privacy measures suitable for several disclosure types and, information loss and predictive performance measures. In this survey, we discuss the main challenges raised by privacy constraints, describe the main approaches to handle these obstacles, review taxonomies of privacy-preserving techniques, provide a theoretical analysis of existing comparative studies, and raise multiple open issues.

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“…In [5] Gonc ¸alves and Resende formally described the basic concepts of BRKGA alongside a survey of applications of that time. Since them, a considerable number of state-of-the-art applications for optimization problems used BRKGA [7] [8] [9], [10]. The BRKGA metaheuristic was chosen for HVDRP due to the ease of implementation and because it only has one problem dependant component.…”

Section: Brkga Algorithmmentioning

confidence: 99%

Biased Random-key Genetic Algorithm for theHybrid Vehicle-drone Routing Problem for Pick-upand Delivery

Zudio¹,

Coelho²,

Ochi³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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The Hybrid Vehicle drone Routing Problem (HVDRP) was recently introduced as an extension of the classic Vehicle Routing Problem (VRP). In this version, one vehicle is equipped with multiple drones to serve customers with demands for pick-up and delivery. The vehicle travels between stations that serve as parking locations to dispatch drones to attend clients. The drones have limitations in their maximum flight range and carrying capacity. We propose a BRKGA algorithm to solve HVDRP with a decoder component specially tailored to find feasible solutions. The proposed method is empirically analyzed in solution quality through a test set that a mixed-integer programming (MIP) model implementation can optimally solve in reasonable computation time. The computational result shows that the best solution found by BRKGA for each instance of the test set matches the solution quality devised by the MIP implementation. The data also show that the proposed algorithm achieves the best solution consistently through many independent executions. The instance set used and its respective best solutions attained for this work are publicly available.

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Microaggregation heuristic applied to statistical disclosure control

Cited by 10 publications

References 25 publications

Toward a Comparison of Classical and New Privacy Mechanism

Toward a Comparison of Classical and New Privacy Mechanism

Survey on Privacy-Preserving Techniques for Data Publishing

Biased Random-key Genetic Algorithm for theHybrid Vehicle-drone Routing Problem for Pick-upand Delivery

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