A survey of privacy-preserving mechanisms for heterogeneous data types

Cunha, Mariana; Mendes, Ricardo; Vilela, João P.

doi:10.1016/j.cosrev.2021.100403

Cited by 49 publications

(31 citation statements)

References 57 publications

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“…Cunha at al. [18] formulated an ontology of methods that distinguish between structured (categorical and numerical data), semi-structured (e.g. graph data, XML), and unstructured data (e.g.…”

Section: The Need For Categorizing Anonymization Approachesmentioning

confidence: 99%

“…Even though there are various methodological developments regarding the protection of sensitive data, the practice often lacks an overview as well as a guide of how to use methods and tools available for data protection. Those few existing overviews are limited and therefore scarcely used [18,77]. Here, we provide an overview based on central criteria describing a context for privacy-preserving data handling, which allows informed decisions in view of the many alternatives.…”

Section: Introductionmentioning

confidence: 99%

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A systematic overview on methods to protect sensitive data provided for various analyses

Templ

Sariyar

2022

Int. J. Inf. Secur.

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In view of the various methodological developments regarding the protection of sensitive data, especially with respect to privacy-preserving computation and federated learning, a conceptual categorization and comparison between various methods stemming from different fields is often desired. More concretely, it is important to provide guidance for the practice, which lacks an overview over suitable approaches for certain scenarios, whether it is differential privacy for interactive queries, k-anonymity methods and synthetic data generation for data publishing, or secure federated analysis for multiparty computation without sharing the data itself. Here, we provide an overview based on central criteria describing a context for privacy-preserving data handling, which allows informed decisions in view of the many alternatives. Besides guiding the practice, this categorization of concepts and methods is destined as a step towards a comprehensive ontology for anonymization. We emphasize throughout the paper that there is no panacea and that context matters.

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Section: The Need For Categorizing Anonymization Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A systematic overview on methods to protect sensitive data provided for various analyses

Templ

Sariyar

2022

Int. J. Inf. Secur.

View full text Add to dashboard Cite

show abstract

“…person-specific data. Person-specific data can be modeled in a variety of styles such as tables, graphs, matrices, traces, logs, images, multimedia, and hybrid [35]. However, we consider personal data represented in a graph form G, where G = (V, E, δ), in our work.…”

Section: Classification In the Scope Of Privacymentioning

confidence: 99%

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

2022

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Owning to the massive growth in internet connectivity, smartphone technology, and digital tools, the use of various online social networks (OSNs) has significantly increased. On the one hand, the use of OSNs enables people to share their experiences and information. On the other hand, this ever-growing use of OSNs enables adversaries to launch various privacy attacks to compromise users’ accounts as well as to steal other sensitive information via statistical matching. In general, a privacy attack is carried out by the exercise of linking personal data available on the OSN site and social graphs (or statistics) published by the OSN service providers. The problem of securing user personal information for mitigating privacy attacks in OSNs environments is a challenging research problem. Recently, many privacy-preserving solutions have been proposed to secure users’ data available over OSNs from prying eyes. However, a systematic overview of the research dynamics of OSN privacy, and findings of the latest privacy-preserving approaches from a broader perspective, remain unexplored in the current literature. Furthermore, the significance of artificial intelligence (AI) techniques in the OSN privacy area has not been highlighted by previous research. To cover this gap, we present a comprehensive analysis of the state-of-the-art solutions that have been proposed to address privacy issues in OSNs. Specifically, we classify the existing privacy-preserving solutions into two main categories: privacy-preserving graph publishing (PPGP) and privacy preservation in application-specific scenarios of the OSNs. Then, we introduce a high-level taxonomy that encompasses common as well as AI-based privacy-preserving approaches that have proposed ways to combat the privacy issues in PPGP. In line with these works, we discuss many state-of-the-art privacy-preserving solutions that have been proposed for application-specific scenarios (e.g., information diffusion, community clustering, influence analysis, friend recommendation, etc.) of OSNs. In addition, we discuss the various latest de-anonymization methods (common and AI-based) that have been developed to infer either identity or sensitive information of OSN users from the published graph. Finally, some challenges of preserving the privacy of OSNs (i.e., social graph data) from malevolent adversaries are presented, and promising avenues for future research are suggested.

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“…To determine which measures are appropriate, it is essential to distinguish between data formats. A very recent survey on heterogeneous data was introduced by Cunha et al (2021). The authors propose a privacy taxonomy that establishes a relation between different types of data and suitable privacy-preserving strategies for the characteristics of those data types.…”

Section: Introductionmentioning

confidence: 99%

Towards a Data Privacy-Predictive Performance Trade-off

Carvalho¹,

Moniz²,

Faria³

et al. 2022

Preprint

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Machine learning is increasingly used in the most diverse applications and domains, whether in healthcare, to predict pathologies, or in the financial sector to detect fraud. One of the linchpins for efficiency and accuracy in machine learning is data utility. However, when it contains personal information, full access may be restricted due to laws and regulations aiming to protect individuals' privacy. Therefore, data owners must ensure that any data shared guarantees such privacy. Removal or transformation of private information (deidentification) are among the most common techniques. Intuitively, one can anticipate that reducing detail or distorting information would result in losses for model predictive performance. However, previous work concerning classification tasks using de-identified data generally demonstrates that predictive performance can be preserved in specific applications. In this paper, we aim to evaluate the existence of a trade-off between data privacy and predictive performance in classification tasks. We leverage a large set of privacy-preserving techniques and learning algorithms to provide an assessment of re-identification ability and the impact of transformed variants on predictive performance. Unlike previous literature, we confirm that the higher the level of privacy (lower *

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A survey of privacy-preserving mechanisms for heterogeneous data types

Cited by 49 publications

References 57 publications

A systematic overview on methods to protect sensitive data provided for various analyses

A systematic overview on methods to protect sensitive data provided for various analyses

A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

Towards a Data Privacy-Predictive Performance Trade-off

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