Cognitive Approach for Location Privacy Protection

Han, Meng; Li, Lei; Xie, Ying; Wang, Jinbao; Duan, Zhuojun; Li, Ji; Yan, Mingyuan

doi:10.1109/access.2018.2805464

Cited by 34 publications

(25 citation statements)

References 45 publications

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“…Nowadays, large shopping malls, supermarkets, and small retail stores offer a wide variety of goods for consumers to choose, which brings rich and convenient shopping experience to people and greatly stimulates and promotes the speed of social development. At present, the operation mode often adopted in the retail market is storekeeper self-operation, consumer self-selection, single duty, and long hours business, and convenience stores can bring convenient and timely shopping experience to consumers and improve the efficiency of life services [ 1 ]. At the beginning, barcode technology was mainly used to identify commodities.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

2020

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In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of the method, in the study, the features of color, shape, and texture of commodity images are first analyzed, and the basic structure of deep convolutional neural network (CNN) model is analyzed. Then, 50,000 pictures containing different commodities are constructed to verify the recognition effect of the model. Finally, the MS-CNN model is taken as the research object for improvement to explore the influence of label errors (p = 0.03, 0.05, 0.07, 0.09, 0.12) with different parameter settings and different probabilities (size of convolutional kernel, Dropout rate) on the recognition accuracy of MS-CNN model, at the same time, a CIR system platform based on MS-CNN model is built, and the recognition performance of salt and pepper noise images with different SNR (0, 0.03, 0.05, 0.07, 0.1) was compared, then the performance of the algorithm in the actual image recognition test was compared. The results show that the recognition accuracy is the highest (97.8%) when the convolution kernel size in the MS-CNN model is 2*2 and 3*3, and the average recognition accuracy is the highest (97.8%) when the dropout rate is 0.1; when the error probability of picture label is 12%, the recognition accuracy of the model constructed in this study is above 96%. Finally, the commodity image database constructed in this study is used to identify and verify the model. The recognition accuracy of the algorithm in this study is significantly higher than that of the Minitch stochastic gradient descent algorithm under different SNR conditions, and the recognition accuracy is the highest when SNR = 0 (99.3%). The test results show that the model proposed in this study has good recognition effect in the identification of commodity images in scenes of local occlusion, different perspectives, different backgrounds, and different light intensity, and the recognition accuracy is 97.1%. To sum up, the CIR platform based on MS-CNN model constructed in this study has high recognition accuracy and robustness, which can lay a foundation for the realization of subsequent intelligent commodity recognition technology.

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Section: Introductionmentioning

confidence: 99%

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

2020

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“…If that condition holds, the mobile user will change his/her identity with that user's identity and vice versa. Another method to protect personal identity in social distancing scenarios is individual information privacy protection through indirect-or proxy-request as proposed in [143]. In particular, instead of directly submitting a request to the server, a mobile user can have his/her social friends through the available social network resources, i.e., trusted social media, to distribute his/her request anonymously to the server.…”

Section: ) Personal Identity Protectionmentioning

confidence: 99%

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

et al. 2020

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This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I [1], an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs.

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“…Most recently, considering the data from both the cyber-physical world and online social networks, Refs. [35][36][37] proposed methods to provide a comprehensive solution to the problem of influence maximization.…”

Section: Influence Maximization Problemmentioning

confidence: 99%

Spreading social influence with both positive and negative opinions in online networks

Han

et al. 2019

Big Data Min. Anal.

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Social networks are important media for spreading information, ideas, and influence among individuals. Most existing research focuses on understanding the characteristics of social networks, investigating how information is spread through the "word-of-mouth" effect of social networks, or exploring social influences among individuals and groups. However, most studies ignore negative influences among individuals and groups. Motivated by the goal of alleviating social problems, such as drinking, smoking, and gambling, and influence-spreading problems, such as promoting new products, we consider positive and negative influences, and propose a new optimization problem called the Minimum-sized Positive Influential Node Set (MPINS) selection problem to identify the minimum set of influential nodes such that every node in the network can be positively influenced by these selected nodes with no less than a threshold of Â. Our contributions are threefold. First, we prove that, under the independent cascade model considering positive and negative influences, MPINS is APX-hard. Subsequently, we present a greedy approximation algorithm to address the MPINS selection problem. Finally, to validate the proposed greedy algorithm, we conduct extensive simulations and experiments on random graphs and seven different realworld data sets that represent small-, medium-, and large-scale networks.

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Cognitive Approach for Location Privacy Protection

Cited by 34 publications

References 45 publications

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

Spreading social influence with both positive and negative opinions in online networks

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