Search

With the widespread application of location-based services (LBS), the issue of location privacy protection has garnered increasing attention. To address the challenge of balancing service quality and privacy protection in existing LBS privacy protection algorithms, this paper proposes a location privacy protection scheme based on Hyperledger Fabric. By leveraging user collaboration probability indistinguishability, the scheme ensures service quality while significantly enhancing privacy protection capabilities. Specifically, the proposed scheme utilizes the multi-channel feature of Hyperledger Fabric to design a multi-channel privacy protection algorithm, effectively addressing the issues of single points of failure and data misuse in centralized architectures. By integrating distributed K-anonymity techniques with blockchain technology, a novel user collaboration probability indistinguishability algorithm is introduced, which effectively resists homogeneity attacks and background knowledge attacks. Theoretical analysis and simulation experiments validate the effectiveness of the proposed scheme. Experimental results demonstrate that the scheme achieves efficient protection of user location privacy while maintaining service quality and exhibits strong resistance to various attacks. [ABSTRACT FROM AUTHOR]

Abstract The identification and tracking for model pigs, as a vital research content for studying the habits of model pigs, drawed more and more considerable attention. To fulfill people requirements for the effectiveness of the non-significant model pig tracking in breeding environment, a Camshift tracking approach based on correlation probability graph, i.e., C a m T r a c o r −P G , is proposed in this paper, in which the correlation probability graph is introduced to achieve target positioning and tracking. Technically, acquiring images through a vision sensor, according to the circular arrangement of pixels in the inverse probability projection graph, and multiplying the inverse projection probability value of a pixel by its surrounding pixels could obtain the weighted sum. Then, the target projection grayscale graph is established by utilizing the correlation probability value for positioning, identification, and tracking of model pigs. Finally, extensive experiments are conducted to validate reliability and efficiency of our approach.

The identification and tracking for model pigs, as a vital research content for studying the habits of model pigs, drawed more and more considerable attention. To fulfill people requirements for the effectiveness of the non-significant model pig tracking in breeding environment, a Camshift tracking approach based on correlation probability graph, i.e., CamTracor−PG, is proposed in this paper, in which the correlation probability graph is introduced to achieve target positioning and tracking. Technically, acquiring images through a vision sensor, according to the circular arrangement of pixels in the inverse probability projection graph, and multiplying the inverse projection probability value of a pixel by its surrounding pixels could obtain the weighted sum. Then, the target projection grayscale graph is established by utilizing the correlation probability value for positioning, identification, and tracking of model pigs. Finally, extensive experiments are conducted to validate reliability and efficiency of our approach. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As location-based services (LBSs) require users to report their location to obtain services, many people are starting to realize the exposed to high privacy threats. In order to preserve the privacy, a great deal of privacy preserving algorithms is preserved in the last several years. Unfortunately, existing privacy preserving algorithms for LBSs usually mainly consider generalizing or cloaking the location and neglect the correlation probability between the query and location, and the adversary can use the probability to guess the real location. In this paper, based on the concept of differential privacy, we propose a mechanism for achieving probability indistinguishable, and then based on this mechanism a location-shift scheme to obfuscate the correlation between the query and location is proposed. To address the correlation probability obfuscation, we first show the correlation attack model with four potential methods based on the correlation probability. Then we study the proposed attacks on several existing algorithms designed for snapshot as well as continues services and define a formalization of probability indistinguishable to propose a countermeasure with location-shift, which can mitigate this type of attacks. At last, we verify the security of our location-shift scheme with entropy and mutual information, and the empirical evaluations further verify the effectiveness and efficiency of our scheme. [ABSTRACT FROM AUTHOR]

Identifying traffic bottlenecks is a prerequisite to alleviate traffic congestion in urban networks. However, the state-of-the-art methods for bottleneck identification stay at the segment level which assume bottlenecks only result from congested road segments (CRSs). The possibility that a turning direction in a road segment could be a bottleneck has not been thoroughly investigated. In addition, most existing techniques only focus on the congestion degree of road segments, however ignoring the correlations between CRSs and their underlying congestion propagations. This study proposed a framework for turn-level bottleneck identification in large-scale road networks using vehicle trajectory data. First, filtered GPS trajectory data was used to identify CRSs and their congestion start time. Then, congestion correlation graphs and corresponding spanning trees were constructed by investigating forward and backward correlations between CRSs. These directional correlations were later modeled via a Bayesian inference approach. Last, congestion correlation cost models were built up to identify turn-level bottlenecks considering the probability of congestion correlation. Both simulation and field experiments were conducted to evaluate the performance of the proposed framework and compare with the state-of-the-art methods. Results reveal that our framework can effectively capture directional correlations between CRSs, and successfully identify turn-level bottlenecks in large-scale networks. [ABSTRACT FROM AUTHOR]

Pyramidal neurons in layer 5 of the neocortex can be differentiated into 3 cell subtypes: 1) short regular spiking (SH), 2) tall regular spiking (TR), and 3) tall burst spiking (TB), based on their morphological and electrophysiological properties. We characterized the functional excitatory local input to these 3 cell subtypes in rat primary visual cortex using laser-scanning photostimulation. Although all cell types received significant input from all cortical layers, SH neurons received stronger input from layer 4 and weaker input from layer 5 than did tall pyramidal cells. However, the laminar input to the 2 populations of tall pyramidal cells was indistinguishable. Simultaneous paired recording were then used to calculate a correlation probability (CP) to infer the proportion of shared input based on the occurrence of simultaneous synaptic potentials. Tall pairs of matched type had significantly higher CPs compared with unmatched pairs, suggesting that subpopulations of layer 4, 5, and 6 neurons preferentially connect to each tall cell type. Hence, this study shows that unconnected but matching pairs of tall pyramidal neurons, but not short pyramidal neurons, receive functional input from different interconnected networks within layers 4, 5, and 6., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)