Biblio

Real-time data transmissions from a vehicle enhance road safety and traffic efficiency by aggregating data in a central server for data analytics. When drivers share their instantaneous vehicular information for a service provider to perform a legitimate task, a curious service provider may also infer private information it has not been authorized for. In this paper, we propose a privacy preservation framework based on the Hilbert Schmidt Independence Criterion (HSIC) to sanitize driving data to protect the vehicle’s trajectory from adversarial inference while ensuring the data is still useful for driver behavior detection. We develop a deep learning model to learn the HSIC sanitizer and demonstrate through two datasets that our approach achieves better utility-privacy trade-offs when compared to three other benchmarks.

The performance of the trust evaluation strategy depends on the accuracy and rationality of the trust evaluation weight system. Trust is a difficult to accurate measurement and quantitative cognition in the heart, the trust of the traditional evaluation method has a strong subjectivity and fuzziness and uncertainty. This paper uses the AHP method to determine the trust evaluation index weight, and combined with grey system theory to build trust gray evaluation model. The use of gray assessment based on the whitening weight function in the evaluation process reduces the impact of the problem that the evaluation result of the trust evaluation is not easy to accurately quantify when the decision fuzzy and the operating mechanism are uncertain.

We consider the problem of preserving the privacy of a set of private parameters while allowing inference of a set of public parameters based on observations from sensors in a network. We assume that the public and private parameters are correlated with the sensor observations via a linear model. We define the utility loss and privacy gain functions based on the Cramér-Rao lower bounds for estimating the public and private parameters, respectively. Our goal is to minimize the utility loss while ensuring that the privacy gain is no less than a predefined privacy gain threshold, by allowing each sensor to perturb its own observation before sending it to the fusion center. We propose methods to determine the amount of noise each sensor needs to add to its observation under the cases where prior information is available or unavailable.

We consider the problem of preserving the privacy of a set of private parameters while allowing inference of a set of public parameters based on observations from sensors in a network. We assume that the public and private parameters are correlated with the sensor observations via a linear model. We define the utility loss and privacy gain functions based on the Cramér-Rao lower bounds for estimating the public and private parameters, respectively. Our goal is to minimize the utility loss while ensuring that the privacy gain is no less than a predefined privacy gain threshold, by allowing each sensor to perturb its own observation before sending it to the fusion center. We propose methods to determine the amount of noise each sensor needs to add to its observation under the cases where prior information is available or unavailable.

BGP is known to have many security vulnerabilities due to the very nature of its underlying assumptions of trust among independently operated networks. Most prior efforts have focused on attacks that can be addressed using traditional cryptographic techniques to ensure authentication or integrity, e.g., BGPSec and related works. Although augmenting BGP with authentication and integrity mechanisms is critical, they are, by design, far from sufficient to prevent attacks based on manipulating the complex BGP protocol itself. In this paper, we identify two serious attacks on two of the most fundamental goals of BGP—to ensure reachability and to enable ASes to pick routes available to them according to their routing policies—even in the presence of BGPSec-like mechanisms. Our key contributions are to 1 formalize a series of critical security properties, 2 experimentally validate using commodity router implementations that BGP fails to achieve those properties, 3 quantify the extent of these vulnerabilities in the Internet's AS topology, and 4 propose simple modifications to provably ensure that those properties are satisfied. Our experiments show that, using our attacks, a single malicious AS can cause thousands of other ASes to become disconnected from thousands of other ASes for arbitrarily long, while our suggested modifications almost completely eliminate such attacks.

Recently, proactive systems such as Google Now and Microsoft Cortana have become increasingly popular in reforming the way users access information on mobile devices. In these systems, relevant content is presented to users based on their context without a query in the form of information cards that do not require a click to satisfy the users. As a result, prior approaches based on clicks cannot provide reliable measurements of user satisfaction with such systems. It is also unclear how much of the previous findings regarding good abandonment with reactive Web searches can be applied to these proactive systems due to the intrinsic difference in user intent, the greater variety of content types and their presentations. In this paper, we present the first large-scale analysis of viewing behavior based on the viewport (the visible fraction of a Web page) of the mobile devices, towards measuring user satisfaction with the information cards of the mobile proactive systems. In particular, we identified and analyzed a variety of factors that may influence the viewing behavior, including biases from ranking positions, the types and attributes of the information cards, and the touch interactions with the mobile devices. We show that by modeling the various factors we can better measure user satisfaction with the mobile proactive systems, enabling stronger statistical power in large-scale online A/B testing.