Biblio

Enabled by the industrial Internet, intelligent transportation has made remarkable achievements such as autonomous vehicles by carnegie mellon university (CMU) Navlab, Google Cars, Tesla, etc. Autonomous vehicles benefit, in various aspects, from the cooperation of the industrial Internet and cyber-physical systems. In this process, users in autonomous vehicles submit query contents, such as service interests or user locations, to service providers. However, privacy concerns arise since the query contents are exposed when the users are enjoying the services queried. Existing works on privacy preservation of query contents rely on location perturbation or k-anonymity, and they suffer from insufficient protection of privacy or low query utility incurred by processing multiple queries for a single query content. To achieve sufficient privacy preservation and satisfactory query utility for autonomous vehicles querying services in cyber-physical systems, this article proposes a novel privacy notion of client-based personalized k-anonymity (CPkA). To measure the performance of CPkA, we present a privacy metric and a utility metric, based on which, we formulate two problems to achieve the optimal CPkA in term of privacy and utility. An approach, including two modules, to establish mechanisms which achieve the optimal CPkA is presented. The first module is to build in-group mechanisms for achieving the optimal privacy within each content group. The second module includes linear programming-based methods to compute the optimal grouping strategies. The in-group mechanisms and the grouping strategies are combined to establish optimal CPkA mechanisms, which achieve the optimal privacy or the optimal utility. We employ real-life datasets and synthetic prior distributions to evaluate the CPkA mechanisms established by our approach. The evaluation results illustrate the effectiveness and efficiency of the established mechanisms.

The Named Data Network (NDN) is a promising network paradigm for content distribution based on caching. However, it may put consumer privacy at risk, as the adversary may identify the content, the name and the signature (namely a certificate) through side-channel timing responses from the cache of the routers. The adversary may identify the content name and the consumer node by distinguishing between cached and un- cached contents. In order to mitigate the timing attack, effective countermeasure methods have been proposed by other authors, such as random caching, random freshness, and probabilistic caching. In this work, we have implemented a timing attack scenario to evaluate the efficiency of these countermeasures and to demonstrate how the adversary can be detected. For this goal, a brute force timing attack scenario based on a real topology was developed, which is the first brute force attack model applied in NDN. Results show that the adversary nodes can be effectively distinguished from other legitimate consumers during the attack period. It is also proposed a multi-level mechanism to detect an adversary node. Through this approach, the content distribution performance can be mitigated against the attack.