Sparse Matrix Masking-based Non-Interactive Verifiable (Outsourced) Computation, Revisited
- Submitting institution
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The University of Surrey
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9023586_3
- Type
- D - Journal article
- DOI
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10.1109/TDSC.2018.2861699
- Title of journal
- IEEE Transactions on Dependable and Secure Computing
- Article number
- -
- First page
- 1
- Volume
- 0
- Issue
- 0
- ISSN
- 1545-5971
- Open access status
- Not compliant
- Month of publication
- -
- Year of publication
- 2018
- URL
-
-
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Privacy-preserving verifiable computation protocols enable clients to outsource sensitive workloads to a server and verify the results without revealing inputs/outputs to the server. Such protocols are important to enable intensive computation on private data in the cloud without leakage. Popular methods use Sparse Matrices (SMs) to achieve this, however the paper showed that in fact no existing SM protocols gave strong privacy guarantees. We proposed modifications to two existing protocols and proved they both hold the privacy property, improving the basis for privacy-preserving computation. Furthermore our modifications maintain the level of high performance and all other properties as the originals.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -