Research Grant Call

The DTC Call for Trust Tech Research Excellence (“DTC Research Grant Call”) is a competitive research funding initiative that seeks to support research projects that advances the science of trust technologies across Singapore-based Institutes of Higher Learning (IHLs)  and Research Institutes (RIs).

DTC invites proposals that have great potential to:
  • Advance the science of trust technologies, and
  • Partner industry to explore and solve systemic real-world problems that once solved, can be scaled for other players in the sector or industry. 


Scope of Research

The proposed opportunities and focus areas shall include, but not limited to, the following:

Trusted Analysis 

The main focus will be related to the sharing of sensitive data in artificial intelligence and machine learning. The emphasis will be on the trust tech enhanced computation without disclosing the actual raw data and/or the analytic model:
  • Searchable Symmetric Encryption (SSE): i) Leak user access / search patterns to servers, and ii) Insufficient complex query expressiveness for search on encrypted data.
  • Full Homomorphic Encryption (FHE): Performance issues due to algorithm complexity and large ciphertext sizes which lead to high computational intensiveness and communication overhead and latency. 
  • Private Set Intersection (PSI): i) Performance and scalability issues when the number of parties involved increases, and ii) limited expressive queries and functionalities (e.g. lack of support for range, threshold, and conjunctive queries).
  • Multi-Party Computation (MPC): Practicability and scalability issues as protocols are communication and computationally intensive, particularly when dealing with a large number of parties or complex computations.
  • Differential Privacy (DP): How to strike a balance between privacy and utility when applying DP.
  • Federated Learning (FL): How to develop mechanisms for collaborative model selection, model aggregation, and model update strategies in FL minimizing impact on training accuracy.
  • Data Synthesis (DS): How to handle complex data types, capture spatio-temporal dependencies, and incorporate privacy-preserving mechanism in data synthesis process.

Trusted Digital ID and Web 3.0

The focus will be on verifiable credentials/documents and transferable records in decentralised environments, track and trace in supply chain, digital identity, and analytics on decentralised finance:
  • Trusted Digital Identity Network with emphasis on credentials and verification for digital ID, digital wallet and credential management, and digital ID network reference architecture.
  • Tokenisation and token standards for digital assets to support security and auditing.
  • Biometric-assisted authentication in Web 3.0.
  • Trust in Web 3.0 applications through blockchain traceability and analytics, risk assessments and verification of smart contracts against legal contracts and the use of self-sovereign identity (SSI).

Trusted Compute

The focus will be on how trusted execution environment (TEE) will impact to the design and analysis of trusted applications. Such impacts include the specialised knowledge and understanding of the security features and programming models offered by the given specific TEE platform. Furthermore, data provenance and evidence will be important to enhance the trust in the computation.
  • Design and analysis of system environment for trusted compute based on applications of TEE.
  • Verifiable Computing (VC) for the correctness in distributed, decentralised infrastructure for specific computation tasks, e.g. e-voting tally.
  • Digital Evidence: An important part of digital trust is the ability to establish accountability and liability in case of fraud, transaction failure or abnormal behaviour. To achieve this, digital evidence borrows digital forensic concepts and techniques that are relevant in a holistic approach to digital trust.

Trusted Accreditation

The focus will be on trusted AI model testing – (i) Research into scientific techniques for testing covering Fairness, Explainability, Robustness, Hallucination, (ii) For various AI / Machine Learning (ML) models, including supervised vs. unsupervised learning, and non- generative vs. non-generative AI, with respect to some of the following challenges.
  • Hallucination – Tackle factually inaccurate or fake outputs in Generative AI.
  • Fairness – No unintended bias: AI system makes same decision even if an attribute changes, and data used to train model is representative.
  • Explainability – Explain behaviour of AI models and/or multi-modal models to understand how the inner mechanics. impact the generated output. Examples of problem include lack of transparency, non-deterministic outputs, high dimensionality, and overfitting.
  • Robustness – Address issues relevant to robustness e.g., include non-adversarial robustness and privacy related attacks.


Full details of the scope for the grant call can be found in the DTC Research Grant Call Rules and Guidelines.


Evaluation Criteria

Proposals should clearly state the following:

  • Alignment of proposal to DTC's objectives and direction. 
  • Explaining novelty of research and the needle-moving research challenge that the proposal will solve.
  • Explaining potential industry application or impact. 
  • Justifying benefit of the research to digital trust.
  • Explaining relevance of research to Singapore.


Funding Quantum and Duration

Funding for each selected proposal is up to S$2 million (inclusive of 30% indirect research costs). The duration of support is up to three 3 years.

Opening date for submission (softcopy via email)30 June 2023
Submission deadline30 August 2023, 5:00 pm
Evaluation and approval of proposals August – October 2023
Release of outcome and award30 November 2023
Project commencementDecember 2023 onwards

Guides and Documents


Contact Information

For any queries, please contact [email protected].

DTC Research Grant call is now closed.