Dynamic Watermarking-Based Integrity Protection of Homomorphically Encrypted Databases – Application to Outsourced Genetic Data

Niyitegeka, David; Coatrieux, Gouenou; Bellafqira, Reda; Génin, Emmanuelle; Franco-Contreras, Javier

doi:10.1007/978-3-030-11389-6_12

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…CC #18: DB outsourcing problems: Because so many DB's are now being outsourced, there are serious concerns about the data's accuracy and safety. Clients will have to relinquish management of the information they have outsourced [58,59].…”

Section: Issues In Databasesmentioning

confidence: 99%

“…The data extraction and synthesis process led to a list of 22 challenges, which were narrowed down to 20 upon external reviewer's recommendations, as shown in Table 10. The most common challenges were Data Leakage/Privacy Challenges (63), Poor Authentication System (59), DB Intruders (58), and Authorized/Malicious User Threats (36). These highlighted issues allow companies to evaluate their current security strengths and weaknesses and develop solutions promptly.…”

Section: Practical Implications Of Researchmentioning

confidence: 99%

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Advancing database security: a comprehensive systematic mapping study of potential challenges

et al. 2023

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The value of data to a company means that it must be protected. When it comes to safeguarding their local and worldwide databases, businesses face a number of challenges. To systematically review the literature to highlight the difficulties in establishing, implementing, and maintaining secure databases. In order to better understand database system problems, we did a systematic mapping study (SMS). We’ve analyzed 100 research publications from different digital libraries and found 20 issues after adopting inclusion and exclusion criteria. This SMS study aimed to identify the most up-to-date research in database security and the different challenges faced by users/clients using various databases from a software engineering perspective. In total, 20 challenges were identified related to database security. Our results show that “weak authorization system”, “weak access control”, “privacy issues/data leakage”, “lack of NOP security”, and “database attacks” as the most frequently cited critical challenges. Further analyses were performed to show different challenges with respect to different phases of the software development lifecycle, venue of publications, types of database attacks, and active research institutes/universities researching database security. The organizations should implement adequate mitigation strategies to address the identified database challenges. This research will also provide a direction for new research in this area.

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Section: Issues In Databasesmentioning

confidence: 99%

Section: Practical Implications Of Researchmentioning

confidence: 99%

Advancing database security: a comprehensive systematic mapping study of potential challenges

et al. 2023

View full text Add to dashboard Cite

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“…Homomorphic encryption (HE) is another mechanism used to protect genomic data. HE allows performing linear operations, such as additions and multiplications over encrypted data while ensuring that the decrypted results are equal to the ones carried out on clear data [33], [34]. Recently, many methods to conduct privacy-preserving computation of GWAS using homomorphic encryption have been proposed [17]- [19].…”

Section: A Related Workmentioning

confidence: 99%

Privacy-Preserving Genome-Wide Association Study for Rare Mutations - A Secure FrameWork for Externalized Statistical Analysis

Bellafqira¹,

Ludwig²,

Niyitegeka³

et al. 2020

IEEE Access

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This paper proposes a new privacy-preserving framework to perform rare variant casecontrol association tests with information provided by two parties: a Genomic Research Unit (GRU) with sequencing data from individuals affected by a disease D (cases); a Genomic Research Center (GRC) with sequencing data from healthy individuals (controls). To identify genes with rare variants involved in D, GRU needs to compare cases against controls using association tests (genome-wide association study). The main originality of our proposal is twofold. First, it positions GRC as a proxy between GRU and the server. Doing so makes it possible to use classical cryptographic tools to securely conduct association tests with no computation complexity increase, contrarily to actual state of the art proposals which are of very high complexity being based on homomorphic encryption, for instance. In particular, we show how sensitive data confidentiality can be ensured with secret key based cryptographic hashing with no need to modify statistical algorithms. In our protocol the server simply conducts statistical analyses on partially hashed data. Secondly, we introduce a novel privacy constraint: GRU's identity should remain unknown to the server as this knowledge can give it clues about GRU's data (e.g., diseases and genes of interest). We exhibit how Pretty Good Privacy (PGP) can be used to solve this problem. We illustrate our protocol in the case of one rare variant association test, the Weighted-Sum Statistic (WSS) algorithm, carried out on real genetic data. This secure WSS achieves the same accuracy as its nonsecure version with no increase of complexity. Furthermore, we establish that our protocol can be extended to the different rare variant association tests available in the literature.

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“…DNN watermarking is a promising solution for ownership protection of ML models [48][49][50][51][52][53]. Inspired by multimedia and database watermarking [54][55][56][57][58], it consists of introducing a secret change into the model parameters or behavior during its training in order to enable its identification in the future. As multimedia content watermarking, DNN watermarking must respect some requirements to be effective for IP protection.…”

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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Dynamic Watermarking-Based Integrity Protection of Homomorphically Encrypted Databases – Application to Outsourced Genetic Data

Cited by 5 publications

References 16 publications

Advancing database security: a comprehensive systematic mapping study of potential challenges

Advancing database security: a comprehensive systematic mapping study of potential challenges

Privacy-Preserving Genome-Wide Association Study for Rare Mutations - A Secure FrameWork for Externalized Statistical Analysis

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

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