Computer-controlled systems (3rd ed.)
Computer-controlled systems (3rd ed.)
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Differential privacy under continual observation
Proceedings of the forty-second ACM symposium on Theory of computing
Private memoirs of a smart meter
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Private and Continual Release of Statistics
ACM Transactions on Information and System Security (TISSEC)
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Enhancing Privacy and Accuracy in Probe Vehicle-Based Traffic Monitoring via Virtual Trip Lines
IEEE Transactions on Mobile Computing
| Hi-index | 0.00 |
New solutions proposed for the monitoring and control of large-scale systems increasingly rely on sensitive data provided by end-users. As a result, there is a need to provide guarantees that these systems do not unintentionally leak private and confidential information during their operation. Motivated by this context, this paper discusses the problem of releasing a dynamic model describing the aggregate input-output dynamics of an ensemble of subsystems coupled via a common input and output, while controlling the amount of information that an adversary can infer about the dynamics of the individual subsystems. Such a model can then be used as an approximation of the true system, e.g., for controller design purposes. The proposed schemes rely on the notion of differential privacy, which provides strong and quantitative privacy guarantees that can be used by individuals to evaluate the risk/reward trade-offs involved in releasing detailed information about their behavior.