An efficient format-independent watermarking framework for large-scale data sets

Rani, Sapana; Halder, Raju

doi:10.1016/j.eswa.2022.118085

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…Among measures that have been proposed to protect genomic data, distortion-free watermarking and fingerprinting are two technologies that have gained much more attention in many applications [ 87 ]; however our review analysis reveals that they are less investigated in the GWAS privacy-preserving context. While there is a lack of works satisfying the requirements of both copyright protection and treats tracking in federated learning [ 28 ], this survey will be an opportunity to boost the attention of future researchers for designing new frameworks based on watermarking and fingerprinting [ 63 ].…”

Section: Discussion Challenges Visionmentioning

confidence: 99%

Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision

Aherrahrou,

Tairi,

Aherrahrou

2024

Briefings in Bioinformatics

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Genome-wide association studies (GWAS) serve as a crucial tool for identifying genetic factors associated with specific traits. However, ethical constraints prevent the direct exchange of genetic information, prompting the need for privacy preservation solutions. To address these issues, earlier works are based on cryptographic mechanisms such as homomorphic encryption, secure multi-party computing, and differential privacy. Very recently, federated learning has emerged as a promising solution for enabling secure and collaborative GWAS computations. This work provides an extensive overview of existing methods for GWAS privacy preserving, with the main focus on collaborative and distributed approaches. This survey provides a comprehensive analysis of the challenges faced by existing methods, their limitations, and insights into designing efficient solutions.

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Section: Discussion Challenges Visionmentioning

confidence: 99%

Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision

Aherrahrou,

Tairi,

Aherrahrou

2024

Briefings in Bioinformatics

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“…The former maintains data intact, while the latter performs watermark embedding by replacing some bits of the protected data with the marks. Some distortion-free techniques propose interesting approaches, such as protecting datasets with different formats beyond relational data [14]. Nevertheless, given that our work focuses on analyzing the preservation of distortion-based robust watermarks, such approaches are out of this research's scope.…”

Section: Introductionmentioning

confidence: 99%

Study of the Watermark Source’s Topology Role on Relational Data Watermarking Robustness

Gort,

Olliaro,

Cortesi

2024

IEEE Access

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Accessing relational databases through web services increases risks of piracy and tampering. Watermarking guarantees data protection without constraining their access and management. Researchers traditionally compare the extracted watermark with the embedded one after simulating attacks to analyze its robustness. However, the literature has not addressed how much watermark signal is retained after querying multi-word textual attributes and how topological features of the watermark affect robustness. In this work, we fill this gap by evaluating a semantics-preserving approach. We execute different classes of queries in three data sets, including an actual database of a public administration. The results show that 99.99% of the marks can be detected when considering a watermark source with a topological factor close to or equal to 1. As valuable contributions, we formally defined the watermark source's topological factor and the corrosion degree of the watermark detection. This work offers hints to data owners to adjust detection parameters, increasing the chances of spotting the watermark despite having, as evidence, data highly damaged due to malicious operations.INDEX TERMS Ownership proof, plagiarism detection, relational data, robust watermarking, watermark topology.

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A zero-watermarking scheme based on spatial topological relations for vector dataset

Ren¹,

Guo²,

Zhu³

et al. 2023

Expert Systems with Applications

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An efficient format-independent watermarking framework for large-scale data sets

Cited by 5 publications

References 42 publications

Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision

Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision

Study of the Watermark Source’s Topology Role on Relational Data Watermarking Robustness

A zero-watermarking scheme based on spatial topological relations for vector dataset

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