The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Anonysense: privacy-aware people-centric sensing
Abstract
Personal mobile devices are increasingly equipped with the capability to sense the physical world (through cameras, microphones, and accelerometers, for example) and the, network world (with Wi-Fi and Bluetooth interfaces). Such devices offer many new opportunities for cooperative sensing applications. For example, users' mobile phones may contribute data to community-oriented information services, from city-wide pollution monitoring to enterprise-wide detection of unauthorized Wi-Fi access points. This people-centric mobile-sensing model introduces a new security challenge in the design of mobile systems: protecting the privacy of participants while allowing their devices to reliably contribute high-quality data to these large-scale applications.
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
Cites
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
Tor: The Second-Generation Onion Router
Dingledine, R., Mathewson, N., & Syverson, P. (2004) · doi:10.21236/ada465464 · Cited by 4,104
Cited by
A framework for protecting worker location privacy in spatial crowdsourcing
To, H., Ghinita, G., & Shahabi, C. (2014) · Proceedings of the VLDB Endowment · doi:10.14778/2732951.2732966 · Cited by 361
PRISM: platform for remote sensing using smartphones
Das, T., Mohan, P., Padmanabhan, V. N., Ramjee, R., & Sharma, A. (2010) · Proceedings of the 8th international conference on Mobile systems, applications, and services · doi:10.1145/1814433.1814442 · Cited by 274
Data fusion in cyber-physical-social systems: State-of-the-art and perspectives
Wang, P., Yang, L. T., Li, J., Chen, J., & Hu, S. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.11.002 · Cited by 127
Location privacy models in mobile applications: Conceptual view and research directions
Damiani, M. L. (2014) · GeoInformatica · doi:10.1007/s10707-014-0205-7 · Cited by 34
Smartphone‐based public health information systems: Anonymity, privacy and intervention
Clarke, A., & Steele, R. (2015) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23356 · Cited by 28
Metadata
- Authors
- Cornelius, C., Kapadia, A., Kotz, D., Peebles, D., Shin, M., & Triandopoulos, N.
- Venue
- Proceedings of the 6th international conference on Mobile systems, applications, and services
- Publisher
- ACM
- Published
- 2008-06-17
- Pages
- 211-224
- OpenAlex
- W2146697949
- Cited by
- 377
- References
- 2
- FWCI
- 18.11
- 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: Anonysense: privacy-aware people-centric sensing. https://app.cochat.ai/papers/10.1145/1378600.1378624
- The paper
- Cornelius, C., Kapadia, A., Kotz, D., Peebles, D., Shin, M., & Triandopoulos, N. (2008). Anonysense: privacy-aware people-centric sensing. Proceedings of the 6th international conference on Mobile systems, applications, and services. https://doi.org/10.1145/1378600.1378624
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.
