A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based...

Area of Science:

• Computer Science
• Data Privacy
• Cybersecurity

Background:

• Analyzing large-scale spatial-temporal k-anonymity datasets in location-based services (LBS) presents privacy risks.
• Existing spatial-temporal k-anonymity methods are insufficient against sophisticated inference attacks targeting sensitive knowledge.

Purpose of the Study:

• To develop a novel on-line spatial-temporal k-anonymity method resistant to destination location prediction attacks.
• To enhance the privacy of LBS data by progressively hiding privacy-sensitive sequence rules.

Main Methods:

• Defined a destination location prediction attack model based on privacy-sensitive sequence rules.
• Proposed an on-line spatial-temporal k-anonymity method involving off-line rule mining and on-line dataset generation.
• Employed generalization and avoidance principles to progressively hide sensitive rules in extended anonymity datasets.

Main Results:

• The proposed method is faster and more effective in hiding sensitive rules compared to traditional methods.
• It demonstrates fewer side effects regarding the generation of new sensitive rules.
• Performance variations with the parameter K value were identified, optimizing the balance between hiding sensitive rules and minimizing side effects.

Conclusions:

• The novel on-line spatial-temporal k-anonymity method effectively mitigates inference attacks in LBS.
• The approach offers improved privacy protection with manageable side effects, outperforming traditional methods.
• Parameter tuning (K value) allows for tailored privacy-security trade-offs.