A high-efficient and low-cost secure AMBA framework utilizing configurable data encryption modeling against probe attacks

Wang, Kai; Li, Wei; Liu, Yanjiang; Chen, Tao; Liu, Nan; Xia, Xianzhao

doi:10.1587/elex.18.20210105

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…e random linear coding scheme is adopted to construct the encryption key model of media corpus data storage information. e ciphertext construction and key design of database storage information are carried out under the logistics chaotic mapping, and the optimal encryption of database storage information is realized under the piecewise linear combination model [15]. e encryption and decryption key of the media corpus data storage information is set to H, the random phase reorganization method is used to encode and design the key of media corpus data information, the ciphertext protocol is constructed in logistics chaotic mapping, the block encryption method is used to encode the symbols of media corpus data, and the entanglement state model of media corpus data is established.…”

Section: Media Corpus Data Encryption Applicationmentioning

confidence: 99%

Construction and Application of Media Corpus Based on Big Data

Yin

2021

International Journal of Antennas and Propagation

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Aiming at the problems of poor data quality and low application rate in the construction of existing media corpus, this paper proposes the construction and application research of media corpus based on big data. Media corpus data are collected, the data are divided into four categories, the heuristic data item column sorting algorithm is introduced to sort all collection processes, the minimum value of data item collection rate is determined, on this basis, the maximum value of quantity in media corpus is determined, and data collection is realized in media corpus data through sliding window. Then, the state characteristics and probability distribution of feature data are determined by dynamic Bayesian network, the relationship between the state variables and dimensions of media corpus data is determined, and the media corpus data state is processed by component to complete the preprocessing of media corpus data; finally, through the application research of storage and encryption of the designed database through big data technology, the storage structure data and encryption secret key are designed to realize the construction and application of media corpus. The experimental results show that the data quality of the media corpus constructed by the proposed method is high, and its application rate has been improved to a certain extent.

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Section: Media Corpus Data Encryption Applicationmentioning

confidence: 99%

Construction and Application of Media Corpus Based on Big Data

Yin

2021

International Journal of Antennas and Propagation

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“…The health status of users and living habits can be inferred by attackers. Therefore, how to protect users' location privacy has always been an important issue concerned by the researchers (Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing

Kong

Chen

et al. 2022

Front. Neurorobot.

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In the recent years, with the rapid development of science and technology, robot location-based service (RLBS) has become the main application service on mobile intelligent devices. When people use location services, it generates a large amount of location data with real location information. If a malicious third party gets this location information, it will cause the risk of location-related privacy disclosure for users. The wide application of crowdsensing service has brought about the leakage of personal privacy. However, the existing privacy protection strategies cannot adapt to the crowdsensing environment. In this paper, we propose a novel location privacy protection based on the Q-learning particle swarm optimization algorithm in mobile crowdsensing. By generalizing tasks, this new algorithm makes the attacker unable to distinguish the specific tasks completed by users, cuts off the association between users and tasks, and protects users' location privacy. The strategy uses Q-learning to continuously combine different confounding tasks and train a confounding task scheme that can output the lowest rejection rate. The Q-learning method is improved by particle swarm optimization algorithm, which improves the optimization ability of the method. Experimental results show that this scheme has good performance in privacy budget error, availability, and cloud timeliness and greatly improves the security of user location data. In terms of inhibition ratio, the value is close to the optimal value.

show abstract

A high-efficient and low-cost secure AMBA framework utilizing configurable data encryption modeling against probe attacks

Cited by 2 publications

References 27 publications

Construction and Application of Media Corpus Based on Big Data

Construction and Application of Media Corpus Based on Big Data

Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing

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