Robust and Imperceptible Watermarking Scheme for GWAS Data Traceability

Bellafqira, Reda; Al-Ghadi, Musab; Génin, Emmanuelle; Coatrieux, Gouenou

doi:10.1007/978-3-031-25115-3_10

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Bellafqira et al . [ 32 ] developed a watermarking method to ensure the security of genetic data in the context of GWAS, with the main focus on identifying the origin of the attack. Öksüz et al .…”

Section: Software-based Solutionsmentioning

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: Software-based Solutionsmentioning

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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“…In principle, this type of attack is referred as illegal steganography and involves modifying more or less imperceptibly images to hide some piece of data into the image pixels directly. Notice that, as defined, steganography focuses on secret communication and differs from watermarking whose aim is to hide a message into host data to support various security services such as integrity [6] , traceability [7] , fight against information leaks and so on [8][9][10][11][12] . With watermarking, everyone knows there is a message or, equivalently, a watermark embedded to protect the host data.…”

Section: Introductionmentioning

confidence: 99%

Global texture sensitive convolutional transformer for medical image steganalysis

Zhou,

Chen,

et al. 2024

Multimedia Systems

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The use of medical images by hackers or illegal organizations as a vehicle for information leakage refers to steganography. Exchanged between PACS or communicated during telemedicine sessions, images are modified to hide data. Such leaks through stego-images may result in the disclosure of doctors' or patients' personal data, or of sensitive hospital data posing thus major risks in terms of privacy and security of the information system. In this paper, to detect these illegal image-based communications, we propose a steganalysis approach, the originality of which relies on a novel neural network GTSCT-Net. This one first extracts texture features as global texture features based on location specificity of different parts in image and then extracts possible steganographic information by composing mutlihead self-attention and deep convolution blocks. It also offers easier convergence and higher accuracy on a lower information embedding rate. Comparative experiments on private and public datasets show that the performance of GTSCT-Net for medical image intrusion detection is separately up to 10.12% and 2.97% better than recent advanced steganography detectors.

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“…Pruning Attack: Setting the less useful weights of the model to zero. [15,[59][60][61][62][63][64][64][65][66][67][68][69][70][71] and Black-Box [65,[72][73][74][75][76][77][78][79][80][81][82][83][84][85] watermarking. Each technique is defined by the type of access to the model parameters during the verification process.…”

mentioning

confidence: 99%

“…In the White-Box setting, we assume that the owner will have full access to the model (architecture, parameters, activation maps...). In this way, to insert a watermark into a DNN, the owner will hide a piece of information b in the form of a binary string or an image (e.g., Quick Response code) into the model's parameters [15,71,86], activation maps [65,67,68] or by adding a passport layer [66,87]. As formulated in [68], a White-Box watermarking scheme is defined as follows:…”

mentioning

confidence: 99%

When Federated Learning Meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection

Lansari,

Bellafqira,

Kapusta

et al. 2023

MAKE

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Federated learning (FL) is a technique that allows multiple participants to collaboratively train a Deep Neural Network (DNN) without the need to centralize their data. Among other advantages, it comes with privacy-preserving properties, making it attractive for application in sensitive contexts, such as health care or the military. Although the data are not explicitly exchanged, the training procedure requires sharing information about participants’ models. This makes the individual models vulnerable to theft or unauthorized distribution by malicious actors. To address the issue of ownership rights protection in the context of machine learning (ML), DNN watermarking methods have been developed during the last five years. Most existing works have focused on watermarking in a centralized manner, but only a few methods have been designed for FL and its unique constraints. In this paper, we provide an overview of recent advancements in federated learning watermarking, shedding light on the new challenges and opportunities that arise in this field.

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Robust and Imperceptible Watermarking Scheme for GWAS Data Traceability

Cited by 4 publications

References 23 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

Global texture sensitive convolutional transformer for medical image steganalysis

When Federated Learning Meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection

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