Protecting location privacy for outsourced spatial data in cloud storage.

Area of Science:

• Computer Science
• Data Security
• Geographic Information Systems

Background:

• Cloud computing and location-aware devices generate vast amounts of spatial data.
• Outsourcing spatial data to cloud storage raises significant privacy concerns.
• Existing spatial transformation methods like the standard Hilbert curve (SHC) lack sufficient security analysis.

Purpose of the Study:

• To propose and evaluate novel methods for enhancing the location privacy of outsourced spatial data.
• To improve upon the security limitations of the standard Hilbert curve (SHC).
• To introduce a formal framework for measuring privacy disclosure risks in spatial transformation.

Main Methods:

• Development of an index modification method for the standard Hilbert curve, termed SHC(*).
• Introduction of a density-based space-filling curve (DSC) as an alternative privacy-preserving method.
• Formal definition of indistinguishability and attack models for privacy risk assessment.
• Evaluation of proposed methods against SHC using defined security and performance metrics.

Main Results:

• Both SHC(*) and DSC demonstrate superior security in protecting location privacy compared to SHC.
• The proposed methods partially violate the distance-preserving property of SHC to achieve enhanced security.
• DSC exhibits the best performance in terms of index generation efficiency.
• Formal analysis confirms the reduced privacy disclosure risk offered by SHC(*) and DSC.

Conclusions:

• Modified spatial transformation techniques like SHC(*) and DSC offer improved privacy protection for outsourced spatial data.
• DSC provides a strong balance between enhanced location privacy and efficient index generation.
• Further research into spatial data privacy is crucial given the increasing reliance on cloud services and location-aware technologies.