Enhancing the Security and Privacy of Self-Sovereign Identities on Hyperledger Indy Blockchain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Self-sovereign identities provide user autonomy and immutability to individual identities and full control to their identity owners. The immutability and control are possible by implementing identities in a decentralized manner on blockchains that are specially designed for identity operations such as Hyperledger Indy. As with any type of identity, self-sovereign identities too deal with Personally Identifiable Information (PII) of the identity holders and comes with the usual risks of privacy and security. This study examined certain scenarios of personal data disclosure via credential exchanges between such identities and risks of man-in-the-middle attacks in the blockchain based identity system Hyperledger Indy. On the basis of the findings, the paper proposes the following enhancements: 1) A novel attribute sensitivity score model for self-sovereign identity agents to ascertain the sensitivity of attributes shared in credential exchanges 2) A method of mitigating man-in-the-middle attacks between peer self-sovereign identities and 3) A novel quantitative model for determining a credential issuer's reputation based on the number of issued credentials in a window period, which is then utilized to calculate an overall confidence level score for the issuer.
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.
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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.013 | 0.048 |
| Research integrity | 0.000 | 0.000 |
| 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 it