Memorizing Algorithm

Cyber Crime ◽  
2013 ◽  
pp. 600-617
Author(s):  
Quynh Chi Truong ◽  
Anh Tuan Truong ◽  
Tran Khanh Dang
Keyword(s):  
Data Mining ◽  
Service Providers ◽  
Rapid Development ◽  
Adaptive Grid ◽  
Location Based Services ◽  
Location Information ◽  
Data Mining Techniques ◽  
Location Data ◽  
Spatial Grid ◽  
Theoretical Analyses

The rapid development of location-based services, which make use of the location information of the user, presents both opportunities and challenges. Users can benefit from these services; however, they must often disclose their location information, which may lead to privacy problems. In this regard, the authors propose a solution with a memorizing algorithm, using trusted middleware that organizes space in an adaptive grid where it cloaks the user’s location information in an anonymization area before sending it to the service providers. This newly introduced memorizing algorithm calculates on the spatial grid to decrease the overlapped areas as much as possible, which helps conceal users’ locations. This solution protects the user’s privacy while using the service, but also against data mining techniques with respect to their history location data. Experimental results with a user activities map establishes this theoretical analyses as well as the practical value of the proposed solution.

Memorizing Algorithm

2010 ◽  
Vol 2 (4) ◽  
pp. 65-86 ◽  
Author(s):  
Quynh Chi Truong ◽  
Anh Tuan Truong ◽  
Tran Khanh Dang
Keyword(s):  
Data Mining ◽  
Service Providers ◽  
Rapid Development ◽  
Adaptive Grid ◽  
Location Based Services ◽  
Location Information ◽  
Data Mining Techniques ◽  
Location Data ◽  
Spatial Grid ◽  
Theoretical Analyses

The rapid development of location-based services, which make use of the location information of the user, presents both opportunities and challenges. Users can benefit from these services; however, they must often disclose their location information, which may lead to privacy problems. In this regard, the authors propose a solution with a memorizing algorithm, using trusted middleware that organizes space in an adaptive grid where it cloaks the user’s location information in an anonymization area before sending it to the service providers. This newly introduced memorizing algorithm calculates on the spatial grid to decrease the overlapped areas as much as possible, which helps conceal users’ locations. This solution protects the user’s privacy while using the service, but also against data mining techniques with respect to their history location data. Experimental results with a user activities map establishes this theoretical analyses as well as the practical value of the proposed solution.

Memorizing Algorithm

2012 ◽  
pp. 242-260
Author(s):  
Quynh Chi Truong ◽  
Anh Tuan Truong ◽  
Tran Khanh Dang
Keyword(s):  
Data Mining ◽  
Service Providers ◽  
Rapid Development ◽  
Adaptive Grid ◽  
Location Based Services ◽  
Location Information ◽  
Data Mining Techniques ◽  
Location Data ◽  
Spatial Grid ◽  
Theoretical Analyses

The rapid development of location-based services, which make use of the location information of the user, presents both opportunities and challenges. Users can benefit from these services; however, they must often disclose their location information, which may lead to privacy problems. In this regard, the authors propose a solution with a memorizing algorithm, using trusted middleware that organizes space in an adaptive grid where it cloaks the user’s location information in an anonymization area before sending it to the service providers. This newly introduced memorizing algorithm calculates on the spatial grid to decrease the overlapped areas as much as possible, which helps conceal users’ locations. This solution protects the user’s privacy while using the service, but also against data mining techniques with respect to their history location data. Experimental results with a user activities map establishes this theoretical analyses as well as the practical value of the proposed solution.

scholarly journals Privacy vs. Reward in Indoor Location-Based Services

2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin
Keyword(s):  
Location Privacy ◽  
Indoor Localization ◽  
Service Providers ◽  
Location Based Services ◽  
Location Information ◽  
Privacy Concerns ◽  
Indoor Location ◽  
Location Data ◽  
Personalized Services ◽  
Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

ILSSPP: Intelligent location sharing system with privacy preserving features for smart cities

2021 ◽  
pp. 1-12
Author(s):  
Gokay Saldamli ◽  
Richard Chow ◽  
Hongxia Jin
Keyword(s):  
Service Providers ◽  
Smart Cities ◽  
User Interaction ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
Fine Grained ◽  
Location Sharing ◽  
Social Network Structure

Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called ILSSPP. The system protects location information from the service provider and yet enables fine grained location-sharing. One main feature of the system is to protect an individual’s social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. ILSSPP protects locations sharing preferences through protocol unification and masking. ILSSPP has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.

scholarly journals Complying with Privacy Legislation: From Legal Text to Implementation of Privacy-Aware Location-Based Services

2018 ◽  
Vol 7 (11) ◽  
pp. 442 ◽  
Author(s):  
Mehrnaz Ataei ◽  
Auriol Degbelo ◽  
Christian Kray ◽  
Vitor Santos
Keyword(s):  
Data Privacy ◽  
Location Privacy ◽  
Service Providers ◽  
Location Based Services ◽  
Legal Text ◽  
General Data Protection Regulation ◽  
Location Data ◽  
Privacy Laws ◽  
Technical Issues ◽  
General Data

An individual’s location data is very sensitive geoinformation. While its disclosure is necessary, e.g., to provide location-based services (LBS), it also facilitates deep insights into the lives of LBS users as well as various attacks on these users. Location privacy threats can be mitigated through privacy regulations such as the General Data Protection Regulation (GDPR), which was introduced recently and harmonises data privacy laws across Europe. While the GDPR is meant to protect users’ privacy, the main problem is that it does not provide explicit guidelines for designers and developers about how to build systems that comply with it. In order to bridge this gap, we systematically analysed the legal text, carried out expert interviews, and ran a nine-week-long take-home study with four developers. We particularly focused on user-facing issues, as these have received little attention compared to technical issues. Our main contributions are a list of aspects from the legal text of the GDPR that can be tackled at the user interface level and a set of guidelines on how to realise this. Our results can help service providers, designers and developers of applications dealing with location information from human users to comply with the GDPR.

scholarly journals Privacy Preserving Spatio-Temporal Databases Based on k-Anonymity

2020 ◽  
Vol 3 (SI1) ◽  
pp. SI82-SI94
Author(s):  
Anh Tuan Truong
Keyword(s):  
Data Mining ◽  
Privacy Protection ◽  
Location Privacy ◽  
Location Based Services ◽  
Sensitive Information ◽  
User Privacy ◽  
Location Data ◽  
Related Information ◽  
Spatio Temporal ◽  
Location Privacy Protection

The development of location-based services and mobile devices has lead to an increase in the location data. Through the data mining process, some valuable information can be discovered from location data. In the other words, an attacker may also extract some private (sensitive) information of the user and this may make threats against the user privacy. Therefore, location privacy protection becomes an important requirement to the success in the development of location-based services. In this paper, we propose a grid-based approach as well as an algorithm to guarantee k-anonymity, a well-known privacy protection approach, in a location database. The proposed approach considers only the information that has significance for the data mining process while ignoring the un-related information. The experiment results show the effectiveness of the proposed approach in comparison with the literature ones.

scholarly journals Brand Switching Pattern Discovery by Data Mining Techniques for the Telecommunication Industry in Australia

2016 ◽  
Vol 20 ◽  
Author(s):  
Md Zahidul Islam ◽  
Steven D’Alessandro ◽  
Michael Furner ◽  
Lester Johnson ◽  
David Gray ◽  
...  
Keyword(s):  
Data Mining ◽  
Mobile Phone ◽  
Domain Knowledge ◽  
Service Providers ◽  
Mobile Service ◽  
Data Mining Techniques ◽  
Brand Switching ◽  
Switching Patterns ◽  
Switching Models ◽  
Mobile Phone Usage

There is more than one mobile-phone subscription per member of the Australian population. The number of complaints against the mobile-phone-service providers is also high. Therefore, the mobile service providers are facing a huge challenge in retaining their customers. There are a number of existing models to analyse customer behaviour and switching patterns. A number of switching models may also exist within a large market. These models are often not useful due to the heterogeneous nature of the market. Therefore, in this study we use data mining techniques to let the data talk to help us discover switching patterns without requiring us to use any models and domain knowledge. We use a variety of decision tree and decision forest techniques on a real mobile-phone-usage dataset in order to demonstrate the effectiveness of data mining techniques in knowledge discovery. We report many interesting patterns, and discuss them from a brand-switching and marketing perspective, through which they are found to be very sensible and interesting.

Prognosis for Crop Yield Production by Data Mining Techniques in Agriculture

2019 ◽  
pp. 145-158 ◽  
Author(s):  
Divya Singh ◽  
Dinesh Sharma
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Agricultural Research ◽  
Rapid Development ◽  
Agricultural System ◽  
Yield Prediction ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Yield Production

In agriculture, data mining technique is used for extracting information from a large dataset. The techniques for data mining are used in yield prediction for crop at broader spectrum. Agricultural system is very complex and vast therefore to deal with large data situation is a great factor. Different consultancy, industrial production department, organization related to crops is taking keen interest towards crop yield prediction. Here the focus is on the applicability of data mining techniques in agricultural field. The classification and clustering techniques of data mining are used recently in agriculture field. Data mining technology merged with the rapid development of computer science. This chapter focuses on collecting information and overcome the short comes of manual data handling and prediction of yield results of crop production. Data mining is a prominent agricultural research area for analysis of crop yield. These predictions are a very important in solving agricultural problems for crops.

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