Linear Function Based Transformation Scheme for Preserving Database Privacy in Cloud Computing

Because much interest in spatial database in cloud computing has been attracted, studies on preserving location data privacy in cloud computing have been actively done. However, since the existing spatial transformation schemes are weak to proximity attack, they cannot preserve the privacy of users who enjoy location-based services from the cloud computing. Therefore, a transformation scheme for providing a safe service to users is required. So, 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 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.

[1]  Ramakrishnan Srikant,et al.  Order preserving encryption for numeric data , 2004, SIGMOD '04.

[2]  Vishal R. Shinde,et al.  Outsourced Similarity Search on Metric Data Assets , 2014 .

[3]  Andreas Gutscher.  Coordinate transformation - a solution for the privacy problem of location based services? , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[4]  Ling Liu,et al.  Sharoes: A Data Sharing Platform for Outsourced Enterprise Storage Environments , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[5]  Miyoung Jang,et al.  A k-Nearest Neighbor Search Algorithm for Enhancing Data Privacy in Outsourced Spatial Databases , 2013 .

[6]  Yin Yang,et al.  Authenticated join processing in outsourced databases , 2009, SIGMOD Conference.

[7]  Yunhao Liu,et al.  Ad-hoc Anonymity: Privacy Preservation for Location-based Services in Mobile Networks , 2012, 2012 IEEE 18th International Conference on Parallel and Distributed Systems.

[8]  Yücel Saygin,et al.  Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data , 2011, Int. J. Data Warehous. Min..

[9]  Panos Kalnis,et al.  Enabling search services on outsourced private spatial data , 2009, The VLDB Journal.

[10]  Dimitris Sacharidis,et al.  K-anonymity in the Presence of External Databases , 2022 .

[11]  Jun Gao,et al.  Multiple Sensitive Association Protection in the Outsourced Database , 2010, DASFAA.

[12]  Hakan Hacigümüs,et al.  Providing database as a service , 2002, Proceedings 18th International Conference on Data Engineering.

[13]  Frank Yeong-Sung Lin,et al.  A Study on Information Security Management with Personal Data Protection , 2011, 2011 IEEE 17th International Conference on Parallel and Distributed Systems.

[14]  Won Kim.  Cloud computing architecture , 2013, Int. J. Web Grid Serv..

[15]  Jae-Woo Chang,et al.  A spatial transformation scheme supporting data privacy and query integrity for security of outsourced databases , 2014, Secur. Commun. Networks.