The DOI matches its Crossref record.
Provable data possession at untrusted stores
Abstract
We introduce a model for provable data possession (PDP) that allows a client that has stored data at an untrusted server to verify that the server possesses the original data without retrieving it. The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs. The client maintains a constant amount of metadata to verify the proof. The challenge/response protocol transmits a small, constant amount of data, which minimizes network communication. Thus, the PDP model for remote data checking supports large data sets in widely-distributed storage system.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
An Efficient and Secure Dynamic Auditing Protocol for Data Storage in Cloud Computing
Yang, K., & Jia, X. (2013) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2012.278 · Cited by 632
Blockchain Based Data Integrity Service Framework for IoT Data
Liu, B., Yu, X. L., Chen, S., Xu, X., & Zhu, L. (2017) · 2017 IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2017.54 · Cited by 385
Blockchain-based data preservation system for medical data
Li, H., Zhu, L., Shen, M., Gao, F., Tao, X., & Liu, S. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-0997-3 · Cited by 347
Permacoin: Repurposing Bitcoin Work for Data Preservation
Miller, A., Juels, A., Shi, E., Parno, B., & Katz, J. (2014) · 2014 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2014.37 · Cited by 310
Dziembowski, S., Faust, S., Kolmogorov, V., & Pietrzak, K. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-662-48000-7_29 · Cited by 294
Metadata
- Authors
- Ateniese, G., Burns, R., Curtmola, R., Herring, J., Kissner, L., Peterson, Z., & Song, D.
- Venue
- Proceedings of the 14th ACM conference on Computer and communications security
- Publisher
- ACM
- Published
- 2007-10-28
- Pages
- 598-609
- ISSN
- 1543-7221 (ISSN-L)
- OpenAlex
- W2102881299
- Cited by
- 2,705
- References
- 0
- FWCI
- 18.66
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Provable data possession at untrusted stores. https://app.cochat.ai/papers/10.1145/1315245.1315318
- The paper
- Ateniese, G., Burns, R., Curtmola, R., Herring, J., Kissner, L., Peterson, Z., & Song, D. (2007). Provable data possession at untrusted stores. Proceedings of the 14th ACM conference on Computer and communications security. https://doi.org/10.1145/1315245.1315318
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
