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Partially Collective Spatial Keyword Query Processing Based on Spatial Keyword Similarity
Ah Hyun Lee, Sehwa Park, Seog Park
http://doi.org/10.5626/JOK.2021.48.10.1142
Collective spatial keyword queries return Points of Interest (POI), which are close to the query location and contain all the presented set of keywords. However, existing studies only consider a fixed number of query keywords, which is not adequate to satisfy the user. They do not care about the preference of a partial keyword set, and a flexible keyword set needs to be selected for the preference of each POI. We thus propose a new query, called Partially Collective Spatial Keyword Query, which flexibly considers keywords that fit the preference for each POI. Since this query is a combinatorial optimization problem, the query processing time increases rapidly as the number of POIs increases. Therefore, to address these problems, we propose a keyword-based search technique that reduces the overall search space. Furthermore, we propose heuristic techniques, which include the linear search-based terminal node pruning technique, approximation algorithm, and threshold-based pruning technique.
LEXAI : Legal Document Similarity Analysis Service using Explainable AI
http://doi.org/10.5626/JOK.2020.47.11.1061
Recently, in keeping with the improvement of deep learning, studies on using deep learning a specialized field have diversified. Semantic searching for legal documents is an essential part of the legal field. However, it is difficult to function outside of the service using the expert system because it requires professional knowledge in the relevant field. It is also challenging to establish an automated, semantically similar legal document retrieval environment because the cost of hiring professional human resources is high. While existing retrieval services provide an environment based on expert systems and statistical systems, the proposed method adopts the deep learning method with a classification task. We propose a database system structure that provides searching for legal documents with high semantic similarity using an explainable neural network. The features of these proposed methods show the performance of developing and verifying visual similarity assessment methods for semantic relevance among similar documents.
A Technique of Protecting User Sensitive Partial Trajectory with Local Differential Privacy on the Road Network
http://doi.org/10.5626/JOK.2020.47.7.693
Today, with the proliferation of smartphones and the development of sensor technology, path data, a list of user location data collected from mobile devices, is being manipulated for marketing or efficient algorithm development. However, such indiscriminate collection of location information may cause personal privacy leakage issues. To resolve the problem, many differential privacy techniques have been proposed. However, the previous methods significantly degrade query accuracy if they are applied in the trajectory dataset. Additionally, the differential privacy technique is classified into a curator model and a local model. The local model has advantages of not having a reliable server, but suffers from more noise inserted to reduce query accuracy. This paper classifies vertices into heavy points and light points to resolve the problem of data usability in applying differential privacy to collect road network trajectory data in the local model. Additionally, experiments show that the proposed technique mitigates the degradation of overall data usability while protecting the sensitive data in accordance with the differential privacy standards.
Disassortative Network Distribution Techniques Using Hub Grouping Based On Local Differential Privacy
http://doi.org/10.5626/JOK.2020.47.6.603
With the development of the wireless Internet and popularization of smartphones, many people are using social network services that connect with others in online. Personal data generated by social network services have high value, but comprise sensitive personal information that could potentially result in serious privacy breaches. The existing studies have presented techniques for generating synthetic data similar to the original network data, or anonymous user information. However, the existing techniques have inherent weaknesses in privacy and data utility because such techniques have not considered the characteristics of network graphs formed by relationships with users. In this paper, we propose the privacy-protected social network data distribution techniques by applying local differential privacy techniques that reflect the characteristics on the social network graph. Through experiments with real data, we have shown that the proposed techniques perform better than the existing differentially private social network data distribution techniques.
Single Group Collective Trip Planning Query Processing Using G-tree Index Structures on Road Networks
http://doi.org/10.5626/JOK.2020.47.5.513
In this paper, we discuss Single Group Collective Trip Planning (SGCTP) queries that minimize the overall travel cost in location-based ride sharing services. The SGCTP queries identify a meeting point that minimizes the overall cost of such a trip when a group of users are gathered at a particular point and travel to the destination using one vehicle. Although many researches on collective trip planning queries have been conducted, there is a problem that the query performance is effective only in a specific situation. So, we introduce a baseline method of the SGCTP queries and then, propose an effective pruning technique with a G-tree index structure. Additionally, we analyze that the limitations of the previous studies, and experimental results show that the proposed pruning technique can obtain the optimal query result without being affected by the limitations of the previous studies.
Reverse Collective Spatial Keyword Queries based on G-tree for Road Networks
http://doi.org/10.5626/JOK.2019.46.5.478
With the proliferation of mobile devices and social network services, spatial keyword queries have become a hot research topic. Previous works have focused on collective spatial keyword queries (CSKQs), which find a set of objects that covers the queried keywords and is close to the query location. In addition, analyzing the correlation between two objects has been studied extensively for real-world applications, such as location recommendations, personalized advertisements, and online social marketing services. CSKQ is suitable for supporting these services because it returns a correlated set of objects. However, the existing studies on CSKQ have focused only on the users’ perspective, despite the fact that such applications require the objects’ perspective. To address this problem, we propose a novel spatial keyword query (reverse collective spatial keyword query, RCSKQ) and a query processing technique based on the road network environment with a G-tree index structure.
Data Privacy-Price Negotiation for applying Differential Privacy in Data Market Environments
http://doi.org/10.5626/JOK.2019.46.4.376
Digital data is currently an indispensable resource for making effective decisions. As the value of digital data is increasing, digital markets, where data providers and consumers can deal with data, are also attracting attention as a mean of obtaining that data. However, obtaining the digital data can lead to privacy breaches, which affects individuals’ willingness to provide data. In this study, a fair negotiation method that can set the appropriate price and noise parameter εconsidering the data provider and the consumer in the differentially private data market environment was proposed. A data market framework with a market manager that links the data provider and the consumer is suggested. In addition, a technique of determining the price and noise parameter ε of the data in two phases using matching theory and Rubinstein bargaining is proposed. It is established that the proposed negotiation technique provides an appropriate level of ε and unit price, which satisfy the data provider and the consumer. The proposed technique prevents unfair transactions and can determine the appropriate level of ε and unit price.
Route Recommendation based on Dynamic User Preference on Road Networks
http://doi.org/10.5626/JOK.2019.46.1.77
The current location based services provide maps and nearby information, or provide a route to a specific destination. A route recommendation system recommends the best route that suits the evaluation criteria for each user. The existing personalized path recommendation system recommends the route under the assumption that the user’s preference is constant regardless of the change of the time zone. However, there is a problem in that it does not reflect requirements that important factors to users can be different for each time zone, such as importance of moving distance in morning time and importance of risk in late time. In this paper, we propose a Dijkstra algorithm considering time attributes to overcome this limitation. In addition, we suggest an efficient algorithm that can search the path reflecting the change of the weight of the preference factor according to the time zone using the G-tree index structure that effectively expresses the road network.
A Predictive Query Processing Method Considering the Movement of both a User and Objects
http://doi.org/10.5626/JOK.2018.45.12.1302
Recently, with the increase in use of mobile devices such as smart phones and tablet PCs with GPS, it is possible to analyze a large volume of data aggregated from various sensors. Accordingly, a variety of location-based services (LBSs) have attracted attention. To effectively provide these services, techniques for efficient spatial query processing have been studied. In this paper, we propose a method to overcome the limitation of not returning the desired query result to the user, because existing studies did not consider movement of the user. Specifically, we propose an algorithm to efficiently process a predictive query in the road network that returns the best available K moving objects, in consideration of the time of the user`s moving and that of the user`s waiting. In this process, we apply the technique to gradually expand the range of user and object`s movement simultaneously. Also, an appropriate index structure is used to efficiently process queries even in the road network with a large number of vertices and moving objects. Experimental results reveal the difference in the query result compared to existing studies and also reveal significant results in terms of efficiency.
Privacy Budget Allocation Technique Based on Variable Length Window for Traffic Data Publishing with Differential Privacy in Road Networks
Gunhyung Jo, Kangsoo Jung, Seog Park
http://doi.org/10.5626/JOK.2018.45.9.957
Recently, traffic volume data at every timestamp have been required in many fields such as road design and traffic analysis. Such traffic volume data may contain individual sensitive location information, which leads to privacy violation such as personal route exposure. Differential privacy has the advantage of protecting sensitive personal information in this situation while controlling the data utility by inserting noise to raw data. However, because of the traffic volume data generally would be an infinite size over time, there is a drawback in that data is useless because insufficiently large scaled noise is inserted. In order to overcome this drawback, researches have been conducted on applying the differential privacy technique only to the traffic volume data contained in windows of a certain time range. However, in the previous studies, the length of the window was fixed, inducing a limit whereby the correlation of the road sections and the time-specificity are not considered. In this paper, we propose a variable length window technique considering the correlation between road segments and time-specificity.
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