Project description
Quantum-secure protocols for privacy-preserving AI
As AI systems become more powerful and widely deployed, computation is increasingly delegated across devices, institutions and cloud platforms. In many settings, only one party holds the data while the AI model belongs to another, creating major privacy and security challenges. With the support of the Marie Skłodowska-Curie Actions programme, the QSDL project aims to address this challenge by establishing a practical protocol for information-theory-secure AI computation. By leveraging the quantum nature of light and using conventional telecommunications components, it will protect both clients’ sensitive data and servers’ proprietary AI models by establishing rigorous bounds on information leakage while maintaining high accuracy. The protocol will be demonstrated on real-life devices, paving the way for privacy-preserving applications in healthcare and AI assistance.
Objective
Deep Neural Networks (DNNs) have sparked a revolution across multiple domains. However, the escalating computational demands of advanced DNNs have led to the need for high-power consumption accelerators, which hinders their deployment on low-power devices. To enable advanced DNNs on such devices, the industry has resorted to offloading computationally intensive DNN inference to cloud servers. Nevertheless, this offloading architecture introduces vulnerabilities that compromise data security, presenting a pressing challenge, particularly in AI applications where data privacy is essential.
In response, I propose a pioneering approach that harnesses the quantum nature of light to establish a practical paradigm for information-theoretically secure deep learning, where clients safeguard their sensitive data, and servers protect their proprietary AI models. This project delves into information science, aiming to explore theoretically and experimentally whether quantum science can offer practical advantages in privacy-preserving inner products against limited adversaries. The innovative essence of this proposal lies in the demonstration of quantum private deep learning in Prof. Englund's lab at MIT, utilizing an optical network. The experimental validation of the protocol with real-life healthcare devices in collaboration with Prof. Katz (HUJI) will unlock a new era of machine learning applications, including the secure utilization of AI in healthcare (e.g. IBM's Watson, Aidoc’s aiOS) and AI assistance (e.g. ChatGPT, Bard). This work may also reshape regulations like the EU General Data Protection Regulation.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
- natural sciences computer and information sciences computer security data protection
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering computer hardware quantum computers
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2024-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
91904 Jerusalem
Israel
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.