In the data communication system, the real-time information interaction of communication device increases the risk of privacy sensitive data being tampered with. Therefore, maintaining data security is one of the most important issues in network data communication. Because the timestamp is the most important way to authenticate data in information interaction, it is very necessary to provide timestamp service in the data communication system. However, the existing centralized timestamp mechanism is difficult to provide credible timestamp service, and users can conspire with timestamping servers to forge timestamps. Therefore, this paper designs a distributed timestamp mechanism based on continuous verifiable delay functions. It utilizes multiple independent timestamp servers to provide timestamp services in a distributed model and appends the timestamp to the data once the data is generated. Thus, it can prove that the data already exists at a certain time and ensure the accuracy of the timestamp. Moreover, a digital blind signature based on elliptic curve cryptography is utilized to solve the problem of timestamp forgery in timestamp service. Finally, the security analysis of the scheme ensures the data security of data communication system and the concurrency rate of timestamp. The experimental results also show that the scheme greatly improves the efficiency of digital signatures. 

/pdf/distributed-timestamp-mechanism-based-on-verifiable-delay-j7fx5pze.pdf

Distributed Timestamp Mechanism Based on Verifiable Delay Functions

Vehicular ad hoc networks(VANETs) ensure the road safety by the continuous information interaction among nodes and nodes to road side units (RSUs). Therefore, filtering out the received bogus data becomes an urgent problem. In this paper, an effective data filtering scheme (EDFS) based on context for VANETs is proposed and evaluated. The proposed scheme comprehensively uses outlier detection method and clustering algorithm to detect general bogus data based on context and data characteristics, which does not rely on other third-party infrastructure (such as RSU). Besides, EDFS is capable of resisting Sybil attack using density-based data mining methods, it ensured that bogus data can be effectively filtered in the presence of large amounts of bogus data as well. The effectiveness of the scheme is proved by experiments.

An Effective Data Filtering Scheme Based on Context in VANETs

Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental data over time. In this study, we propose an incremental maximal frequent itemset mining algorithms that integrate subjective interestingness criterion during the process of mining. The proposed framework is designed to deal with incremental data, which usually come at different times. It extends FP-Max algorithm, which is based on FP-Growth method by pushing interesting measures during maximal frequent itemset mining, and performs dynamic and early pruning to leave uninteresting frequent itemsets in order to avoid uninteresting rule generation. The framework was implemented and tested on public databases, and the results found are promising.

/pdf/an-incremental-interesting-maximal-frequent-itemset-mining-72oazvd6.pdf

An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm

Social reliability and sustainable financial development in metropolises have become urgent problems to be solved. Due to its solid importance and imagination, urban cultural industry management and public economy have gradually become the focus of consideration in all fields of society. Organized and coordinated improvement of the urban economy and society and imaginative undertakings have also become the focus of review at all levels of academia and government. This article aims to study and apply the improved algorithm to the urban cultural industry management system and public economic analysis. It proposed the improved algorithm and related methods, the correlation between urban cultural industry management and economy, the improved particle swarm algorithm and the SOM algorithm, and finally, applied the improved SOM algorithm to evaluate the regional economy of a city. The experimental results showed that the contribution rate of the economic evaluation level of the eight economic evaluation indicators to the economic development level of each county (district and city) in the city was different, of which, the cumulative contribution rate of the first four main components reached 83%.

/pdf/urban-cultural-industry-management-system-and-public-1n04ze51.pdf

Urban Cultural Industry Management System and Public Economic Based on Improved Algorithm

In order to avoid casualties and economic losses,the lightning monitoring equipments are used to warn the lightning events. From this point of view,the application of lightning monitoring and warning system is analyzed in this article; the affected parameter L X and its determination methods are studied in detail,combined with the hiting and missing report rates of the lightning monitoring and warning system,the calculation formulas of lightning risk are established. Furthermore,the procedure chart of risk assessment is given,and the main influencing types of lightning risk in the different places where the lightning monitoring and warning system is installed are discussed. Finally,taking a chemical factory as an example,the lightning risk values are analyzed and compared with the assessing one. This paper provides some referential methods of the reasonable risk assessment for the places where the lightning monitoring and warning measures are completed.

Impact of lightning monitoring and warning on lightning risk assessment

https://www.techscience.com/cmc/v66n1/40480/pdf

Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing

https://www.techscience.com/cmc/v65n1/39569/pdf

An Improved Algorithm for Mining Correlation Item Pairs

Location accuracy is an important measurement for evaluation of lightning locating networks, and the location algorithm directly affect the accuracy of the lightning detection results. According to the existed weakness of arrival time difference in traditional location method such as poor ability of resisting error interference and low location accuracy, and iterative algorithms are easy to diverge, an improved DBSCAN algorithm is introduced in location calculation. The performance of the proposed location algorithm was confirmed by regional simulation. The results showed that ADBSCAN does not require manual intervention, and has better clustering effect for lightning location results. The lightning location algorithm based on ADBSCAN overcomes the shortcomings that iterative algorithms are easy to diverge and fall into local optimum, and can solve the lightning points steadily and accurately.

A Lightning Location Algorithm Based on Clustering Analysis

The security of user personal information on cloud computing is an important issue for the recommendation system. In order to provide high quality recommendation services, privacy of user is often obtained by untrusted recommendation systems. At the same time, malicious attacks often use the recommendation results to try to guess the private data of user. This paper proposes a hybrid algorithm based on NMF and random perturbation technology, which implements the recommendation system and solves the protection problem of user privacy data in the recommendation process on cloud computing. Compared with the privacy protection algorithm of SVD, the elements of the matrix after the decomposition of the new algorithm are non-negative elements, avoiding the meaninglessness of negative numbers in the matrix formed by texts, images, etc., and it has a good explanation for the local characteristics of things. Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of protecting users' personal privacy on cloud computing.

Yongzhen Ren

Papers

Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing

An Improved Algorithm for Mining Correlation Item Pairs

A Lightning Location Algorithm Based on Clustering Analysis

NMF-Based Privacy-Preserving Collaborative Filtering on Cloud Computing

Radiomics features from whole thyroid gland tissue for prediction of cervical lymph node metastasis in the patients with papillary thyroid carcinoma.