Self-Identifying Sensor Data
| Title: | Self-Identifying Sensor Data |
| Author: |
Chong, Stephen N; Vaughan, Jeffrey A.; Skalka, Christian
Note: Order does not necessarily reflect citation order of authors. |
| Citation: | Chong, Stephen, Christian Skalka, and Jeffrey A. Vaughan. 2010. Self-identifying sensor data. Proceedings of the Ninth International Conference on Information Processing in Sensor Networks (IPSN): April 12-16, 2010, Stockholm, Sweden. |
| Full Text & Related Files: |
ipsn10-self-id-data.pdf (379.7Kb; PDF) 
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| Abstract: | Public-use sensor datasets are a useful scientific resource with the unfortunate feature that their provenance is easily disconnected from their content. To address this we introduce a technique to directly associate provenance information with sensor datasets. Our technique is similar to traditional watermarking but is intended for application to unstructured datasets. Our approach is potentially imperceptible given sufficient margins of error in datasets, and is robust to a number of benign but likely transformations including truncation, rounding, bit-flipping, sampling, and reordering. We provide algorithms for both one-bit and blind mark checking. Our algorithms are probabilistic in nature and are characterized by a combinatorial analysis. |
| Published Version: | doi:10.1145/1791212.1791223 |
| Other Sources: | http://people.seas.harvard.edu/~chong/pubs/ipsn10-self-id-data.pdf |
| Terms of Use: | This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP |
| Citable link to this page: | http://nrs.harvard.edu/urn-3:HUL.InstRepos:8207503 |
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Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
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