Enhancing data privacy in financial services : The role of zero-knowledge proofs and federated AI
Bibliographic record
Abstract
This paper analyses the challenges of balancing anonymity, utility and security in financial services. It argues that the traditional approach of using clearinghouses to enhance utility has come at the expense of anonymity. However, the advent of privacy-enhancing technologies like zero-knowledge proofs and federated AI has begun to minimise these trade-offs. The paper provides a case study of Merit Protocol, a company that is using these technologies to address the problem of predatory payday loans. Merit Protocol’s platform allows employers to pre-underwrite loans for their employees without sharing sensitive data. This approach empowers employers to support their employees’ financial needs while maintaining privacy and reducing dependency on traditional credit agencies. The paper concludes by discussing the challenges that the financial services industry must address in order to fully realise the potential of privacy-enhancing technologies. These challenges include navigating legacy compliance frameworks and improving the ease of use of these technologies. Readers can expect to gain a deeper understanding of the challenges of balancing anonymity, utility and security in financial services. They will also learn about the potential of privacy-enhancing technologies to address these challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.014 | 0.027 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".