A New Spatial Transformation Scheme for Preventing Location Data Disclosure in Cloud Computing

2016 ◽  
pp. 1752-1776
Author(s):  
Min Yoon ◽  
Hyeong-il Kim ◽  
Miyoung Jang ◽  
Jae-Woo Chang
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Success Rate ◽  
Data Privacy ◽  
Spatial Database ◽  
Data Distribution ◽  
Location Based Services ◽  
Spatial Transformation ◽  
Location Data

Because much interest in spatial database for cloud computing has been attracted, studies on preserving location data privacy have been actively done. However, since the existing spatial transformation schemes are weak to a proximity attack, they cannot preserve the privacy of users who enjoy location-based services in the cloud computing. Therefore, a transformation scheme is required for providing a safe service to users. We, in this chapter, propose a new transformation scheme based on a line symmetric transformation (LST). The proposed scheme performs both LST-based data distribution and error injection transformation for preventing a proximity attack effectively. Finally, we show from our performance analysis that the proposed scheme greatly reduces the success rate of the proximity attack while performing the spatial transformation in an efficient way.

A New Spatial Transformation Scheme for Preventing Location Data Disclosure in Cloud Computing

2014 ◽  
Vol 10 (4) ◽  
pp. 26-49 ◽  
Author(s):  
Min Yoon ◽  
Hyeong-il Kim ◽  
Miyoung Jang ◽  
Jae-Woo Chang
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Success Rate ◽  
Data Privacy ◽  
Spatial Database ◽  
Data Distribution ◽  
Location Based Services ◽  
Spatial Transformation ◽  
Location Data

Because much interest in spatial database for cloud computing has been attracted, studies on preserving location data privacy have been actively done. However, since the existing spatial transformation schemes are weak to a proximity attack, they cannot preserve the privacy of users who enjoy location-based services in the cloud computing. Therefore, a transformation scheme is required for providing a safe service to users. We, in this paper, propose a new transformation scheme based on a line symmetric transformation (LST). The proposed scheme performs both LST-based data distribution and error injection transformation for preventing a proximity attack effectively. Finally, we show from our performance analysis that the proposed scheme greatly reduces the success rate of the proximity attack while performing the spatial transformation in an efficient way.

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.

Software Performance Analysis Through Resource Provisioning in Cloud Computing

2017 ◽  
Author(s):  
J Deepika ◽  
A P Nivedha Sri ◽  
R Niladevi ◽  
S Sandhiya
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Resource Provisioning ◽  
Software Performance

Performance Analysis of SDN and NFV enabled Mobile Cloud Computing

2020 ◽  
Author(s):  
Joseph Billingsley ◽  
Wang Miao ◽  
Ke Li ◽  
Geyong Min ◽  
Nektarios Georgalas
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Mobile Cloud Computing ◽  
Mobile Cloud

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.

A New Block-Based Data Distribution Mechanism in Cloud Computing

2016 ◽  
Author(s):  
Chandrima Dadi ◽  
Ping Yi ◽  
Zongming Fei ◽  
Hui Lu
Keyword(s):  
Cloud Computing ◽  
Data Distribution ◽  
Block Based

scholarly journals Performance analysis model for big data applications in cloud computing

2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Luis Eduardo Bautista Villalpando ◽  
Alain April ◽  
Alain Abran
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Performance Analysis ◽  
Analysis Model ◽  
Big Data Applications

scholarly journals The Challenges of Institutional Distance: Data Privacy Issues in Cloud Computing

2018 ◽  
Vol 24 (1) ◽  
pp. 161-181 ◽  
Author(s):  
Yashar Abed ◽  
Meena Chavan
Keyword(s):  
Cloud Computing ◽  
Multinational Corporations ◽  
Data Privacy ◽  
Service Providers ◽  
Host Countries ◽  
Case Study Methodology ◽  
Regulatory Constraints ◽  
Cloud Computing Service ◽  
Privacy Issues

Data protection and data privacy are significant challenges in cloud computing for multinational corporations. There are no standard laws to protect data across borders. The institutional and regulatory constraints and governance differ across countries. This article explores the challenges of institutional constraints faced by cloud computing service providers in regard to data privacy issues across borders. Through a qualitative case study methodology, this research compares the institutional structure of a few host countries, with regard to data privacy in cloud computing and delineates a relative case study. This article will also review the cloud computing legal frameworks and the history of cloud computing to make the concept more comprehensible to a layman.

scholarly journals A Secure Ciphertext Retrieval Scheme against Insider KGAs for Mobile Devices in Cloud Storage

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Run Xie ◽  
Chanlian He ◽  
Dongqing Xie ◽  
Chongzhi Gao ◽  
Xiaojun Zhang
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Privacy ◽  
Security Analysis ◽  
Data Retrieval ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Security Issues ◽  
Efficiency Comparison ◽  
Cloud Servers

With the advent of cloud computing, data privacy has become one of critical security issues and attracted much attention as more and more mobile devices are relying on the services in cloud. To protect data privacy, users usually encrypt their sensitive data before uploading to cloud servers, which renders the data utilization to be difficult. The ciphertext retrieval is able to realize utilization over encrypted data and searchable public key encryption is an effective way in the construction of encrypted data retrieval. However, the previous related works have not paid much attention to the design of ciphertext retrieval schemes that are secure against inside keyword-guessing attacks (KGAs). In this paper, we first construct a new architecture to resist inside KGAs. Moreover we present an efficient ciphertext retrieval instance with a designated tester (dCRKS) based on the architecture. This instance is secure under the inside KGAs. Finally, security analysis and efficiency comparison show that the proposal is effective for the retrieval of encrypted data in cloud computing.

Sign in / Sign up

Export Citation Format

Share Document