Conformidade com os Requisitos Legais de Privacidade de Dados: Um Estudo sobre Técnicas de Anonimização

• URL: http://arxiv.org/abs/2507.18360v1
• Date: Thu, 24 Jul 2025 12:31:28 GMT
• Title: Conformidade com os Requisitos Legais de Privacidade de Dados: Um Estudo sobre Técnicas de Anonimização
• Authors: André Menolli, Luiz Fernando Nunes, Thiago A. Coleti,
• Abstract summary: The aim of this study is to analyze anonymization techniques and assess their effectiveness in ensuring compliance with legal requirements and the utility of the data for its intended purpose.<n>The analysis revealed significant variations in effectiveness highlighting the need to balance privacy and utility data.
• Score: 3.2268447897914943
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The protection of personal data has become a central topic in software development, especially with the implementation of the General Data Protection Law (LGPD) in Brazil and the General Data Protection Regulation (GDPR) in the European Union. With the enforcement of these laws, certain software quality criteria have become mandatory, such as data anonymization, which is one of the main aspects addressed by these regulations. The aim of this article is to analyze data anonymization techniques and assess their effectiveness in ensuring compliance with legal requirements and the utility of the data for its intended purpose. Techniques such as aggregation, generalization, perturbation, and k-anonymity were investigated and applied to datasets containing personal and sensitive data. The analysis revealed significant variations in the effectiveness of each method, highlighting the need to balance privacy and data utility.

Related papers

• Optimal Allocation of Privacy Budget on Hierarchical Data Release [48.96399034594329]
  This paper addresses the problem of optimal privacy budget allocation for hierarchical data release.<n>It aims to maximize data utility subject to a total privacy budget while considering the inherent trade-offs between data granularity and privacy loss.
  arXiv  Detail & Related papers  (2025-05-16T05:25:11Z)
• LegiLM: A Fine-Tuned Legal Language Model for Data Compliance [5.256747140296861]
  LegiLM is a novel legal language model specifically tailored for consulting on data or information compliance. It has been fine-tuned to automatically assess whether particular actions or events breach data security and privacy regulations. LegiLM excels in detecting data regulation breaches, offering sound legal justifications, and recommending necessary compliance modifications.
  arXiv  Detail & Related papers  (2024-09-09T02:06:52Z)
• Properties of Effective Information Anonymity Regulations [6.8322083925948185]
  We develop a set of technical requirements for anonymization rules and related regulations. As an exemplar, we evaluate competing interpretations of regulatory requirements from the EU's General Data Protection Regulation.
  arXiv  Detail & Related papers  (2024-08-27T02:34:41Z)
• Collection, usage and privacy of mobility data in the enterprise and public administrations [55.2480439325792]
  Security measures such as anonymization are needed to protect individuals' privacy. Within our study, we conducted expert interviews to gain insights into practices in the field. We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy.
  arXiv  Detail & Related papers  (2024-07-04T08:29:27Z)
• Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks [4.66054169739129]
  In this paper, we explore the balance between preserving data usefulness and ensuring robust privacy safeguards. We share our method for manually pseudonymizing a multilingual radicalization dataset, ensuring performance comparable to the original data.
  arXiv  Detail & Related papers  (2024-06-25T18:30:25Z)
• The Data Minimization Principle in Machine Learning [61.17813282782266]
  Data minimization aims to reduce the amount of data collected, processed or retained. It has been endorsed by various global data protection regulations. However, its practical implementation remains a challenge due to the lack of a rigorous formulation.
  arXiv  Detail & Related papers  (2024-05-29T19:40:27Z)
• A Summary of Privacy-Preserving Data Publishing in the Local Setting [0.6749750044497732]
  Statistical Disclosure Control aims to minimize the risk of exposing confidential information by de-identifying it. We outline the current privacy-preserving techniques employed in microdata de-identification, delve into privacy measures tailored for various disclosure scenarios, and assess metrics for information loss and predictive performance.
  arXiv  Detail & Related papers  (2023-12-19T04:23:23Z)
• Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
  We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation. We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
  arXiv  Detail & Related papers  (2023-04-21T09:03:18Z)
• Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
  Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
  arXiv  Detail & Related papers  (2021-12-21T08:44:05Z)
• Learning to Limit Data Collection via Scaling Laws: Data Minimization Compliance in Practice [62.44110411199835]
  We build on literature in machine learning law to propose framework for limiting collection based on data interpretation that ties data to system performance. We formalize a data minimization criterion based on performance curve derivatives and provide an effective and interpretable piecewise power law technique.
  arXiv  Detail & Related papers  (2021-07-16T19:59:01Z)
• The SPECIAL-K Personal Data Processing Transparency and Compliance Platform [0.1385411134620987]
  SPECIAL EU H 2020 project can be used to represent data policies and data and events sharing. System can verify that data processing and sharing complies with the data subjects consent.
  arXiv  Detail & Related papers  (2020-01-26T14:30:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.