Secret sharing homomorphisms: keeping shares of a secret secret
Proceedings on Advances in cryptology---CRYPTO '86
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Private collaborative forecasting and benchmarking
Proceedings of the 2004 ACM workshop on Privacy in the electronic society
Secure distributed data-mining and its application to large-scale network measurements
ACM SIGCOMM Computer Communication Review
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Privacy-Preserving graph algorithms in the semi-honest model
ASIACRYPT'05 Proceedings of the 11th international conference on Theory and Application of Cryptology and Information Security
Detection and Localization Sensor Assignment with Exact and Fuzzy Locations
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
| Hi-index | 0.00 |
It is now well known that data-fusion from multiple sensors can improve detection and localisation of targets. Traditional data fusion requires the sharing of detailed data from multiple sources. In some cases, the various sources may not be willing to share such detailed information. For instance, current military allies may be willing to share some level of information, but only if they can do so without revealing their secrets. This situation appears relevant for modern sensor networks, which may be comprised of networks from multiple participants. It has previously been shown that localisation of a single target can be performed while preserving location privacy of the sensor nodes. Here we extend this to the case of multiple targets. The novel aspect of the problem is related to the ambiguity in target labels, and how we resolve this ambiguity.