VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

Development of Data Processing Software for NBI Spectroscopic Analysis System

A set of data processing software is presented in this paper for processing NBI spectroscopic data.For better and more scientific managment and querying these data,they are managed uniformly by the NBI data server.The data processing software offers the functions of uploading beam spectral original and analytic data to the data server manually and automatically,querying and downloading all the NBI data,as well as dealing with local LZO data.The set software is composed of a server program and a client program.The server software is programmed in C/C＋＋under a CentOS development environment.The client software is developed under a VC6.0 platform,which offers convenient operational human interfaces.The network communications between the server and the client are based on TCP.With the help of this set software,the NBI spectroscopic analysis system realizes the unattended automatic operation,and the clear interface also makes it much more convenient to offer beam intensity distribution data and beam power data to operators for operation decision-making.

Research on Welding Quality Traceability Model of Offshore Platform Block Construction Process

Quality traceability plays an essential role in assembling and welding offshore platform blocks.The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry.Currently,qualitymanagement remains in the era of primary information,and there is a lack of effective tracking and recording of welding quality data.When welding defects are encountered,it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data.In this paper,a composite welding quality traceability model for offshore platform block construction process is proposed,it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm.By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm,the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems.Furthermore,the model and the quality traceability algorithm are checked by cases in actual working conditions.Verification analyses suggest that the proposed early-warningmodel for welding quality and the algorithmfor optimizing backtracking requests are effective and can be applied to the actual construction process.

Banian: A Cross-Platform Interactive Query System for Structured Big Data

The rapid growth of structured data has presented new technological challenges in the research fields of big data and relational database. In this paper, we present an efficient system for managing and analyzing PB level structured data called Banian. Banian overcomes the storage structure limitation of relational database and effectively integrates interactive query with large-scale storage management. It provides a uniform query interface for cross-platform datasets and thus shows favorable compatibility and scalability. Banian＇s system architecture mainly includes three layers：（1） a storage layer using HDFS for the distributed storage of massive data;（2） a scheduling and execution layer employing the splitting and scheduling technology of parallel database; and（3）an application layer providing a cross-platform query interface and supporting standard SQL. We evaluate Banian using PB level Internet data and the TPC-H benchmark. The results show that when compared with Hive, Banian improves the query performance to a maximum of 30 times and achieves better scalability and concurrency.

LSTM Network-Based Adaptation Approach for Dynamic Integration in Intelligent End-Edge-Cloud Systems

Edge computing, which migrates compute-intensive tasks to run on the storage resources of edge devices, efficiently reduces data transmission loss and protects data privacy. However, due to limited computing resources and storage capacity, edge devices fail to support real-time streaming data query and processing. To address this challenge, first, we propose a Long Short-Term Memory (LSTM) network-based adaptive approach in the intelligent end-edge-cloud system. Specifically, we maximize the Quality of Experience (QoE) of users by automatically adapting their resource requirements to the storage capacity of edge devices through an event mechanism. Second, to reduce the uncertainty and non-complete adaption of the edge device towards the user’s requirements, we use the LSTM network to analyze the storage capacity of the edge device in real time. Finally, the storage features of the edge devices are aggregated to the cloud to re-evaluate the comprehensive capability of the edge devices and ensure the fast response of the user devices during the dynamic adaptation matching process. A series of experimental results show that the proposed approach has superior performance compared with traditional centralized and matrix decomposition based approaches.

Analyses of Political Crisis Impact on Tourism:A Panel Counterfactual Approach with Internet Search Index

Existing research has shown that political crisis events can directly impact the tourism industry.However,the current methods suffer from potential changes of unobserved variables,which poses challenges for a reliable evaluation of the political crisis impacts.This paper proposes a panel counterfactual approach with Internet search index,which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest.It also provides a tool to examine potential channels through which the crisis may affect tourist outflows.This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea.Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place.This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.

Efficient Processing of Skyline Group Queries over a Data Stream

In this paper, we study the skyline group problem over a data stream. An object can dominate another object if it is not worse than the other object on all attributes and is better than the other object on at least one attribute. If an object cannot be dominated by any other object, it is a skyline object. The skyline group problem involves finding k-item groups that cannot be dominated by any other k-item group. Existing algorithms designed to find skyline groups can only process static data. However, data changes as a stream with time in many applications,and algorithms should be designed to support skyline group queries on dynamic data. In this paper, we propose new algorithms to find skyline groups over a data stream. We use data structures, namely a hash table, dominance graph, and matrix, to store dominance information and update results incrementally. We conduct experiments on synthetic datasets to evaluate the performance of the proposed algorithms. The experimental results show that our algorithms can efficiently find skyline groups over a data stream.