Building Privacy-Preserving and Secure Decentralized AI via Cryptography

About the Project

As AI systems scale, privacy, security, and trustworthiness emerge as challenges. For instance, private data may be leaked during model training, and interactions among autonomous entities can lead to vulnerabilities. These concerns are amplified in decentralized AI and multi-agent systems, when multiple parties (e.g., human or agent) might interact without full trust in a centralized party.

This research project aims to develop cryptographic solutions and frameworks that secure AI systems, particularly in decentralized and distributed settings. The project will investigate how advanced and modern cryptographic protocols, such as zero-knowledge proofs, secure multiparty computation, homomorphic encryption, exotic signatures, and their post-quantum versions, can enable secure, privacy-preserving, verifiable, and trustworthy AI. This project will allow a PhD student to contribute to the intersection of applied cryptography, decentralized systems (e.g., blockchains), AI safety, and privacy, with potential impact across domains such as finance and healthcare.

Eligibility

Applicants are expected to have an MSc degree in computer science or other majors related to decentralized systems, AI, cyber security, cryptography, or mathematics.

Outstanding candidates with a Bachelor’s degree from a four-year undergraduate program are also encouraged to apply. Paper publication is not mandatory, but a high-quality publication is helpful to strengthen an application, particularly for overseas candidates seeking studentship funding at the University of Manchester.

Funding

This 3.5-year PhD studentship is open to Home (UK) applicants. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26; subject to annual uplift), and tuition fees will be paid. We expect the stipend to increase each year.

We recommend that you apply early as the advert will be removed once the position has been filled.

The start date for this project is July 2026.

Before you apply

We strongly recommend that you contact the supervisor (Dr Zhipeng Wang) for this project before you apply at zhipeng.wang@manchester.ac.uk. Please include your CV, details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

How to apply

Apply online through our website: https://uom.link/pgr-apply-fap

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.

We recommend that you apply early as the advert might be removed once the position has been filled.