Location Privacy Protection Systems in Presence of Service Quality and Energy Constraints

The wide-ranging application of location-based services (LBSs) through the use of mobile devices and wireless networks has brought about many critical privacy challenges. To preserve the location privacy of users, most existing location privacy-preserving mechanisms (LPPMs) modify their real locations associated with different pseudonyms, which come at a cost either in terms of resource consumption or quality of service, or both. However, we observed that the effect of resource consumption has not been discussed in existing studies. In this paper, we present the user-centric LPPMs against location inference attacks under the consideration of both service quality and energy constraints. Moreover, we modeled the precision-based and dummy-based mechanisms in the context of an existing LPPM framework, and also extended the linear program solutions applicable to them. This study allowed us to specify the LPPMs that decreased the precision of exposed locations or generated dummy locations of the users. Based on this, we evaluated the privacy protection effects of optimal location obfuscation function against an adversary's inference attack function using real mobility datasets. The results indicate that dummy-based mechanisms provide better achievable location privacy under a given combination of service quality and energy constraints, and once a certain level of privacy is reached, both the precision-based and dummy-based mechanisms only perturb the exposed locations. The evaluation results also contribute to a better understanding for the LPPM design strategies and evaluation mechanism as far as the system resource utilization and service quality requirements are concerned.

NIWAS: A non-interactive watermark authentication scheme for industrial field-programmable gate array designs

Except the network attacks, the industrial networked devices in Internet of things are also threatened by intellectual property infringement. Watermarking technique is a prevalent way to avoid this threat. Previous work on authenticating a watermark in industrial intellectual properties easily discloses sensitive information of real embedded watermarks. In this case, the evidence of identifying the ownership of industrial intellectual property may be attacked by the illegal verifiers. Although several watermark detection techniques can address the disclosure of sensitive information in detection procedure, the efficiency of detection is relatively low. Besides, it may yield large communication overhead of multiple authentication rounds. Motivated by the needs of robustness and efficiency, this work proposed a zero-knowledge approach to authenticate ownership of field-programmable gate array intellectual property design in industrial environment, named NIWAS. The prover can convince the verifier that he knows a secret in the suspected intellectual property design via only one interaction. Real locations of watermarks are concealed through location obfuscation. With the received authentication package from the prover, the verifier cannot obtain other useful information about the watermarks. The experiments show that NIWAS achieves high efficiency and robustness of watermark detection.

Efficient Utility Improvement for Location Privacy

The continuously increasing use of location-based services poses an important threat to the privacy of users. A natural defense is to employ an obfuscation mechanism, such as those providing geo-indistinguishability, a framework for obtaining formal privacy guarantees that has become popular in recent years. Ideally, one would like to employ an optimal obfuscation mechanism, providing the best utility among those satisfying the required privacy level. In theory optimal mechanisms can be constructed via linear programming. In practice, however, this is only feasible for a radically small number of locations. As a consequence, all known applications of geo-indistinguishability simply use noise drawn from a planar Laplace distribution. In this work, we study methods for substantially improving the utility of location obfuscation, while maintaining practical applicability as a main goal. We provide such solutions for both infinite (continuous or discrete) as well as large but finite domains of locations, using a Bayesian remapping procedure as a key ingredient. We evaluate our techniques in two real world complete datasets, without any restriction on the evaluation area, and show important utility improvements with respect to the standard planar Laplace approach.