Linear queries can be submitted to a server containing private data. The server provides a response to the queries systematically corrupted using an additive noise to preserve the privacy of those whose data is stored on the server. The measure of privacy is inversely proportional to the trace of the Fisher information matrix. It is assumed that an adversary can inject a false bias to the responses. The measure of the security, capturing the ease of detecting the presence of the false data injection, is the sensitivity of the Kullback-Leiber divergence to the additive bias. An optimization problem for balancing privacy and security is proposed and subsequently solved. It is shown that the level of guaranteed privacy times the level of security equals a constant. Therefore, by increasing the level of privacy, the security guarantees can only be weakened and vice versa. Similar results are developed under the differential privacy framework.

Original languageEnglish
Title of host publicationProceedings of the 57th IEEE Conference on Decision and Control (CDC 2018)
EditorsAndrew R. Teel, Magnus Egerstedt
Place of PublicationPiscataway, NJ, USA
ISBN (Electronic)978-1-5386-1395-5
Publication statusPublished - 2018
EventCDC 2018: 57th IEEE Conference on Decision and Control - Miami, United States
Duration: 17 Dec 201819 Dec 2018


ConferenceCDC 2018: 57th IEEE Conference on Decision and Control
CountryUnited States

ID: 51915181