Query Accuracy Research Articles

A Top-K Query Scheme With Privacy Preservation for Intelligent Vehicle Network in Mobile IoT

The emergence of mobile Internet of Things (IoT) has brought much more convenience to our daily life than ever before, modern intelligent vehicles are usually equipped with various mobile IoT components or devices. However, when we are enjoying the convenience from the rapid development of mobile IoT services, our privacy can be misused by attackers in an easier way. In this paper, we devise a query scheme with intelligent vehicle privacy guarantees, which enables a vehicle to acquire accurate query service from the service provider (SP) without providing its explicit private information, such as location or query interest privacy. And more than that, we introduce network coding for the first time to make our scheme applicable to a more sophisticated top-K query by multiple vehicles cooperation in mobile IoT. Furthermore, we also consider the unnecessary reveal issue of service data at SP side since the data is its asset. Performance analysis and experiments verify the validity of our scheme and demonstrate a better accuracy and efficiency compared with existing solutions in mobile IoT scenario.

Maximum Influential Location Selection With Differentially Private User Locations

The widespread use of mobile devices and social network services has made optimal location queries an important research topic. Previous studies have focused on the problem of maximum influential (Max-inf) location selection, that is, finding a location that can attract as many clients as possible. The location information of each client should be collected to process such a query. However, client location is considered sensitive information. Therefore, a privacy protection technique should be applied to Max-inf problems. Motivated by this, we propose a Max-inf problem query-processing technique with differentially private client location information. Furthermore, we present a Voronoi region-based technique to guarantee query accuracy and a Voronoi envelope-based pruning heuristic to improve query performance.

Intelligent Electronic Management of Library by Radio Frequency Identification Technology

In the information era, the knowledge learned in the campus can hardly meet the needs of the society, so the library has become one of the choices for people to supplement their knowledge. With the increasing demand for libraries, the traditional library management method has been difficult to support the demand. In order to promote the intelligent transformation of the library, the basic structure and principle of Radio Frequency Identification Devices (RFID) technology were briefly introduced in this paper, and the intelligent library management system based on RFID was also introduced. Then the library of Huaqiao University was divided into two regions, and the performance of collection counting, book query and passage efficiency of traditional library management based on magnetic stripe together with bar code and intelligent library management based on RFID were compared. The results showed that the collection counting efficiency of intelligent libraries was much higher than that of traditional libraries, the high accuracy and stability of book query avoided the dislocation and chaos like traditional libraries, and there was no congestion even during rush hours because of the higher passing efficiency.

Mobile P2P-Based Skyline Query Processing over Delay-Tolerant Networks

Skyline query-processing techniques considering various properties in peer to peer (P2P)-based services have become a recent topic of research. In this paper, we propose a new skyline query-processing scheme to improve the query-processing performance and accuracy in a mobile P2P service over delay-tolerant networks. The proposed scheme collects data on the query object from neighboring nodes and establishes a local skyline through static properties to reduce query-processing costs. To improve the query accuracy in a non-uniform distribution environment, the query-dissemination range is expanded by enforcing a query-dissemination range expansion. The performance evaluation conducted to verify the superiority of the proposed scheme demonstrates that it has a better performance compared to the existing schemes.

Free gap information from the differentially private sparse vector and noisy max mechanisms

Noisy Max and Sparse Vector are selection algorithms for differential privacy and serve as building blocks for more complex algorithms. In this paper we show that both algorithms can release additional information for free (i.e., at no additional privacy cost). Noisy Max is used to return the approximate maximizer among a set of queries. We show that it can also release for free the noisy gap between the approximate maximizer and runner-up. This free information can improve the accuracy of certain subsequent counting queries by up to 50%. Sparse Vector is used to return a set of queries that are approximately larger than a fixed threshold. We show that it can adaptively control its privacy budget (use less budget for queries that are likely to be much larger than the threshold) in order to increase the amount of queries it can process. These results follow from a careful privacy analysis.

2059. Antimicrobial Stewardship of Community Parenteral Antimicrobial Therapy: A Health System Approach

BackgroundCommunity parenteral antimicrobial therapy (CoPAT) allows patients to receive intravenous (IV) antimicrobials outside the hospital; however, inappropriate use occurs and can lead to adverse outcomes. In addition, these patients are at high risk of readmission. Our objective was to assess the quality of CoPAT in a large healthcare system in order to guide implementation of an intervention requiring mandatory review by antimicrobial stewardship.MethodsWe identified patients with orders for IV antimicrobials at discharge between January 1 and December 10, 2018. Patients were excluded if transferred to an acute care facility, left against medical advice, or died. 250 patients were selected using a random number generator and reviewed consecutively until 100 confirmed CoPAT encounters were identified. Each encounter was evaluated for evidence of ID consultation, opportunities for stewardship interventions in seven categories (See Table 1), and adverse events such as emergency room (ER) visits and readmissions.ResultsThe query identified 4,642 potential CoPAT discharges from 22 hospitals (see Table 2). 117 encounters were reviewed to reach 100 true CoPAT discharges (85% query accuracy). Of these, 55 (55%) received a formal ID consult, 6 (6%) had an ID pharmacist or ID physician curbside, and 5 (5%) had an ID clinic follow-up appointment scheduled without formal ID consult. Opportunity for stewardship intervention was found in 50 (50%) patients (see Table 1). There were 31 (31%) patients who were seen in the ER (n = 21) and/or re-admitted (n = 19) to the hospital during or shortly after completion of CoPAT, of which 25 (81%) were potentially related to CoPAT, including abnormal laboratory findings, PICC-line complications, and signs or symptoms of infection.ConclusionCoPAT patients are complex with high healthcare utilization. Mandatory ID review of patients receiving CoPAT has the potential to impact 2,000 lives annually in a large health system. Disclosures All authors: No reported disclosures.

Efficient Range Queries for Large-Scale Sensor-Augmented RFID Systems

This paper studies the practically important problem of range query for sensor-augmented RFID systems, which is to classify the target tags according to the ranges specified by the user. The existing RFID protocols that seem to address this problem suffer from either low time-efficiency or the information corruption issue. To overcome their limitations, we first propose a basic classification protocol called Range Query (RQ) , in which each tag pseudo-randomly chooses a slot from the time frame and uses the ON-OFF Keying modulation to reply its range identifier. Then, RQ employs a collaborative decoding method to extract the tag range information from singleton and even collision slots. The numerical results reveal that the number of queried ranges significantly affects the performance of RQ . To optimize the number of queried ranges, we further propose the PartitionM (ii) it is immune to the interference from unexpected tags, and does not suffer information corruption issue. We use USRP and WISP tags to conduct a set of experiments, which demonstrate the feasibility of RQ + PM . Extensive simulation results reveal that RQ + PM can ensure 100% query accuracy, and reduce the time cost as much as 40% when comparing with the state-of-the-art protocols.

Local trend discovery on real-time microblogs with uncertain locations in tight memory environments

This paper presents GeoTrend+; a system approach to support scalable local trend discovery on recent microblogs, e.g., tweets, comments, online reviews, and check-ins, that come in real time. GeoTrend+ discovers top-k trending keywords in arbitrary spatial regions from recent microblogs that continuously arrive with high rates and a significant portion has uncertain geolocations. GeoTrend+ distinguishes itself from existing techniques in different aspects: (1) Discovering trends in arbitrary spatial regions, e.g., city blocks. (2) Considering both exact geolocations, e.g., accurate latitude/longitude coordinates, and uncertain geolocations, e.g., district-level or city-level, that represents a significant portion of past years microblogs. (3) Promoting recent microblogs as first-class citizens and optimizes different components to digest a continuous flow of fast data in main-memory while removing old data efficiently. (4) Providing various main-memory optimization techniques that are able to distinguish useful from useless data to effectively utilize tight memory resources while maintaining accurate query results on relatively large amounts of data. (5) Supporting various trending measures that effectively capture trending items under a variety of definitions that suit different applications. GeoTrend+ limits its scope to real-time data that is posted during the last T time units. To support its queries efficiently, GeoTrend+ employs an in-memory spatial index that is able to efficiently digest incoming data and expire data that is beyond the last T time units. The index also materializes top-k keywords in different spatial regions so that incoming queries can be processed with low latency. In peak times, the main-memory optimization techniques are employed to shed less important data to sustain high query accuracy with limited memory resources. Experimental results based on real data and queries show the scalability of GeoTrend+ to support high arrival rates and low query response time, and at least 90+% query accuracy even under limited memory resources.

A Hybrid Aggregate Index Method for Trajectory Data

The aggregate query of moving objects on road network keeps being popular in the ITS research community. The existing methods often assume that the sampling frequency of the positioning devices like GPS or roadside radar is dense enough, making the result’s uncertainty negligible. However, such assumption is not always tenable, especially in the extreme occasions like wartime. Regarding this issue, a hybrid aggregate index framework is proposed in this paper, in order to perform aggregate queries on massive trajectories that are sampled sparsely. Firstly, this framework uses an offline batch processing component based on the UPBI-Sketch index to acquire each object’s most likely position between two continuous sampling instants. Next, it introduces the AMH+-Sketch index to processing the aggregate operation online, making sure each object is counted only once in the result. The experimental results show that the hybrid framework can ensure the query accuracy by adjusting the parameters L and U of AMH+-Sketch index and its space storage advantage becomes more and more obvious when the data scale is very large.

PGAS: Privacy-preserving graph encryption for accurate constrained shortest distance queries

The constrained shortest distance (CSD) query is used to determine the shortest distance between two vertices of a graph while ensuring that the total cost remains lower than a given threshold. The virtually unlimited storage and processing capabilities of cloud computing have enabled the graph owners to outsource their graph data to cloud servers. However, it may introduce privacy challenges that are difficult to address. In recent years, some relevant schemes that support the shortest distance query on the encrypted graph have been proposed. Unfortunately, some of them have unacceptable query accuracy, and some of them leak sensitive information that jeopardizes the graph privacy. In this work, we propose Privacy-preserving Graph encryption for Accurate constrained Shortest distance queries, called PGAS. This solution is capable of providing accurate CSD queries and ensures the privacy of the graph data. Besides, we also propose a secure integer comparison protocol and a secure minimum value protocol that realize two kinds of operations on encrypted integers. We provide theoretical security analysis to prove that PGAS achieves CQA-2 Security with less privacy leakage. In addition, the performance analysis and experimental evaluation based on real-world dataset show that PGAS achieves 100% accuracy with acceptable efficiency.

Research on a Novel Improved KMP Fuzzy Query Algorithm

The rapid development of computer technology has led to an increasing demand for database management information systems. Most of the data queries existing in the current system use accurate query methods, which leads to inefficient query and cannot solve the fuzzy matching problem between strings. Based on the Knuth-Morris-Pratt algorithm, this paper introduces the concept of ambiguity and proposes an improved KMP fuzzy query algorithm, which is applied to the disease query system to verify the feasibility of the algorithm. The improved KMP fuzzy query algorithm not only has a high matching speed between strings, but also satisfies the fuzzy matching between strings. Compared with the traditional BF algorithm, KMP algorithm and other algorithms, the improved KMP fuzzy query algorithm has superiority in terms of fuzzy matching.

Hollow-tree: a metric access method for data with missing values

Similarity search is fundamental to store and retrieve large volumes of complex data required by many real world applications. A useful mechanism for such concept is the query-by-similarity. Based on their topological properties, metric similarity functions can be used to index sets of data which can be queried effectively and efficiently by the so-called metric access methods. However, data produced by various application domains and the varying data types handled often lead to missing data, hence, they do not follow the metric similarity requirements. As a consequence, missing data cause distortions in the index structure and yield bias in the query answer. In this paper, we propose the Hollow-tree, a novel access method aimed at successfully retrieving data with missing attribute values. It employs new strategies for indexing and searching data elements, capable of handling the missing data issues when the cause of missingness is ignorable. The indexing strategy is based on a family of distance functions that allow measuring the distance between elements with missing values, along with a set of policies able to organize the elements in the index without causing distortions to its internal structure. The searching strategy employs fractal dimension property of the data to achieve accurate query answer while considering data with missing values part of the response. Results from experiments performed on a variety of real and synthetic data sets showed that, while other metric access methods deteriorate with small amounts of missing values, the Hollow-tree maintains a remarkable performance with almost 100% of precision and recall for range queries and more than 90% for k-nearest neighbor queries, for up to 40% of missing values.

External Plagiarism Detection based on Human Behaviors in Producing Paraphrases of Sentences in English and Persian Languages

With the advent of the internet and easy access to digital libraries, plagiarism has become a major issue. Applying search engines is one of the plagiarism detection techniques that converts plagiarism patterns to search queries. Generating suitable queries is the heart of this technique and existing methods suffer from lack of producing accurate queries, Precision and Speed of retrieved results. This research proposes a framework called ParaMaker. It generates accurate paraphrases of any sentence, similar to human behaviors and sends them to a search engine to find the plagiarism patterns. For English language, ParaMaker was examined against six known methods with standard PAN2014 datasets. Results showed an improvement of 34% in terms of Recall parameter while Precision and Speed parameters were maintained. In Persian language, statements of suspicious documents were examined compared to an exact search approach. ParaMaker showed an improvement of at least 42% while Precision and Speed were maintained.

An efficient blind filter: Location privacy protection and the access control in FinTech

Abstract Financial technology(FinTech) is a new item in the financial industry, which has become a popular item that describes novel technologies adopted by the financial service institutions. This term covers a large range of techniques, from data security to financial service. Specially, user privacy protection is generally considered one of the most significant aspects in the financial security domain and preserving data carrying privacy is a critical task in producing a privacy protection strategy, e.g., one of the crucial issues in mobile finance is to ensure the legitimate mobile device users can efficiently search inclusive information from servers without leaking the user privacy. More precisely, more and more mobile finance APP(e.g., AliPay, China Unionpay Quick Pass) has the auxiliary tool or third-party services function that enable users make a location-based services(LBS) query, while the LBS usually carry users’ location privacy and that data of service providers should be accessed by legitimate users. In order to address this problem, in this paper, we propose a privacy-preserving LBS framework which supports the query area is a square area based on the user’s location, and achieves fine-grained access control on the financial service provider data, user’s privacy(especially location privacy), confidentiality of the service provider data, and accurate query result. More precisely, our framework also uses redundant point-of-interesting(POI) records to protect privacy against LBS provider(LBSP), but employs a semi-trusted third party(called proxy) to filter out redundant POI records. We propose a novel blind filter protocol based on comparable attribute-based encryption(CABE) and “transformation” technique, which can filter out the encrypted POI records under the condition that both LBSP and proxy without knowing the user’s location information. In comparison with existing solutions, our framework not only realize access control on service provider data innately, but also incurs lower communication and computation overhead on the user side. The analysis and the experiments indicate that our framework is secure and efficient for mobile devices in terms of computation and the communication overhead.

Reasoning Temporally Attributed Spatial Entity Knowledge Towards Qualitative Inference of Geographic Process

Knowledge discovery with geo-spatial information processing is of prime importance in geomorphology. The temporal characteristics of evolving geographic features result in geo-spatial events that occur at a specific geographic location. Those events when consecutively occur result in a geo-spatial process that causes a phenomenal change over the period of time. Event and process are essential constituents in geo-spatial dynamism. The geo-spatial data acquired by remote sensing technology is the source of input for knowledge discovery of geographic features. This article performs qualitative inference of geographic process by identifying events causing geo-spatial deformation over time. The evolving geographic features and their types have association with spatial and temporal factors. Event calculus-based spatial knowledge formalism allows reasoning over intervals of time. Hence, representation of Event Attributed Spatial Entity (EASE) Knowledge is proposed. Logical event-based queries are evaluated on the formal representation of EASE Knowledge Base. Event-based queries are executed on the proposed knowledge base and when experimented on, real data sets yielded comprehensive results. Further, the significance of EASE-based spatio-temporal reasoning is proved by evaluating with respect to query processing time and accuracy. The enhancement of EASE with a direction for further development to explore its significance towards prediction is discussed towards the end.

Differentially Private Real-time Streaming Data Publication Based on Sliding Window under Exponential Decay

Continuous response of range query on steaming data provides useful information for many practical applications as well as the risk of privacy disclosure. The existing research on differential privacy streaming data publication mostly pay close attention to boosting query accuracy, but pay less attention to query efficiency, and ignore the effect of timeliness on data weight. In this paper, we propose an effective algorithm of differential privacy streaming data publication under exponential decay mode. Firstly, by introducing the Fenwick tree to divide and reorganize data items in the stream, we achieve a constant time complexity for inserting a new item and getting the prefix sum. Meanwhile, we achieve time complicity linear to the number of data item for building a tree. After that, we use the advantage of matrix mechanism to deal with relevant queries and reduce the global sensitivity. In addition, we choose proper diagonal matrix further improve the range query accuracy. Finally, considering about exponential decay, every data item is weighted by the decay factor. By putting the Fenwick tree and matrix optimization together, we present complete algorithm for differentiate private real-time streaming data publication. The experiment is designed to compare the algorithm in this paper with similar algorithms for streaming data release in exponential decay. Experimental results show that the algorithm in this paper effectively improve the query efficiency while ensuring the quality of the query.

<inline-formula> <tex-math notation="LaTeX">$\tau$ </tex-math> </inline-formula>-Safe (<inline-formula> <tex-math notation="LaTeX">$l,k$ </tex-math> </inline-formula>)-Diversity Privacy Model for Sequential Publication With High Utility

Preserving privacy while maintaining high utility during sequential publication for data providers and data users in mathematical statistics, scientific researching, and organizations making decisions play an important role recently. The $\tau $ -safety model is the state-of-the-art model in sequential publication. However, it is based on the generalization technique, which has some drawbacks such as heavy information loss and difficulty of supporting marginal publication. Besides, the privacy of individuals is the major aspect that needs to be protected in privacy preserving data publishing. In this paper, to protect the privacy of individuals in sequential publication, we develop a new $\tau $ -safe ( $l,k$ )-diversity privacy model based on generalization and segmentation by record anonymity satisfying $l$ -diversity and individual anonymity satisfying $k$ -anonymity. This privacy model ensures that each record’s signatures keep consistency or have no intersection in all releases. It can get high data utility while resisting the linking attacks due to arbitrary updates. In addition, it can also be applied to a dataset where individual has multiple records and arbitrary marginal publication. The results of our experiments show that the proposed privacy model achieves better anonymization quality and query accuracy in comparison with the $m$ -invariance and $\tau $ -safety model in the sequential publication with arbitrary updates.

Node Similarity Top-k Query of the Large-Scale Dynamic Graph With Weak Repeated Path Constraint

Node similarity Top-k query has always been a hot topic in the graph field. The existing node similarity Top-k query methods often need to traverse the whole graph, so its iterative calculation process is very inefficient on the large-scale graph. Besides, when the graph changes dynamically, it is difficult to update the query process and results adaptively, leading to the low accuracy of the query results. To solve those problems, in this paper, we propose a node similarity Top-k query method of the large-scale dynamic graph with weak repeated path constraint. In this method, we first divide the similarity relation according to the correlation degree of nodes in the base graph. Then, we put forward the direct similarity and the generic similarity, meanwhile, give their definition and expression, prove the isolation of direct similarity query in the base graph based on the direct similarity relation. And then a quick query method for the direct similarity Top-k attached result set of $i$ step walk is given on the basis of the value of Top-k. When the attached result set of the direct similarity query cannot satisfy the strict Top-k query requirement, by setting weak repeated path multilevel constraint and introducing the idea of the Monte Carlo simulation, we put forward single-edge weaken factor and change the base graph into many single-direction graphs by weak pageRank random walk mechanism, and then realize the cumulative calculation for the generic similarity Top-k complete result set in single-direction graph set. Finally, according to the dynamic characteristics of the graph, based on the principle of local adaptation, we set the trigger update strategy of the base graph and the linkage update strategy of the weak single-direction graph set to ensure the accuracy of the query and minimize the updating maintenance cost. The experiments show that the method in this paper has great advantages in query efficiency, query accuracy, and update efficiency.

Improving NoSQL Storage Schema Based on Z-Curve for Spatial Vector Data

NoSQL database can provide massive, high concurrency, and scalable services for storing different types of data. HBase, a type of NoSQL database, in which columns are grouped into column families, is very suitable for storing semi-structured or unstructured spatial vector data. However, since there are few rules and constraints to be followed for the NoSQL database, the design of storage schema for spatial data based on NoSQL is difficult. In this paper, based on our early work, an improved Z-curve storage schema is proposed for spatial vector data. According to our new schema, row key of a geometric object is the Z-curve code of the spatial grids intersected with the geometric object. Moreover, geometric objects with the same row key are stored in a column family. Our proposed method has two features. First, geometric objects adjacent in the location are adjacent in physical storage. Second, redundant exists in storage for improving query accuracy. In our experiments, we compare the improved Z-curve storage schema with a Quadtree storage schema, an R-tree storage schema, and the previous Z-curve storage schema. Query response time, memory usage, and the query accuracy of spatial query on point and range are used to verify the validity of our proposed method. The experimental results show that the two storage schemas based on Z-curve achieve higher query efficiency than the two storage schemas based on tree-the Quadtree storage schema and the R-tree storage schema. More importantly, the query results of the improved Z-curve schema are completely correct, while the query results of the previous Z-curve schema are not.

Dynamic Release of Big Location Data Based on Adaptive Sampling and Differential Privacy

Data releasing is a key part bridging between the collection of big data and their applications. Traditional methods release the static version of dataset or publish the snapshot with a fixed sampling interval, which cannot meet the dynamic query requirements and query precision for big data. Moreover, the quality of published data cannot reflect the characteristics of the dynamic changes of big data, which often leads to subsequent data analysis and mining errors. This paper proposes an adaptive sampling mechanism and privacy protection method for the release of big location data. In order to reflect the dynamic change of data in time, we design an adaptive sampling mechanism based on the proportional-integral-derivative (PID) controller according to the temporal and spatial correlation of the location data. To ensure the privacy of published data, we propose a heuristic quad-tree partitioning method as well as a corresponding privacy budget allocation strategy. Experiments and analysis prove that the adaptive sampling mechanism proposed in this paper can effectively track the trend of dynamic changes of data, and the designed differential privacy method can improve the accuracy of counting query and enhance the availability of published data under the premise of certain privacy intensity. The proposed methods can also be readily extended to other areas of big data release applications.