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A Customizable k-Anonymity Model for Protecting Location Privacy - smartech gatech

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The document presents a customizable k-anonymity model designed to protect location privacy in location-based services (LBSs). It introduces a novel spatio-temporal cloaking algorithm called CliqueCloak, which allows mobile users to specify their desired level of anonymity and tolerances for spatial and temporal inaccuracies. The model aims to anonymize messages from mobile nodes while ensuring that the anonymity requirements are met without significantly compromising performance. The paper discusses the challenges of implementing this model, compares it with existing approaches, and provides experimental results demonstrating its effectiveness in maintaining privacy while optimizing service quality.

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What is the problem with k-anonymity?

The concern of k-anonymity is with the re-identification of a single individual in an anonymized data set. There are two re-identification scenarios for a single individual: 1 Re-identify a specific individual (known as the prosecutor re-identification scenario).

What is the k-anonymity method?

K-anonymity is a property of a dataset that indicates the re-identifiability of its records. A dataset is k-anonymous if quasi-identifiers for each person in the dataset are identical to at least k 1 other people also in the dataset.

What are the advantages of k-anonymity?

K anonymity prevents PII from being disclosed, and individuals from being identified within datasets. This form of PII masking makes it easier for organizations to protect consumer, employee, or patient privacy, especially while sharing data with third parties, or using it for software testing.

What is the difference between k-anonymity and differential privacy?

(1) k-anonymity does not induce randomization, the information in the dataset could be deduced by attackers. -differential privacy should have a good privacy-preserving performance over k-anonymity. (2) k-anonymity does not perform as well when applying to high dimensional dataset.

What is the difference between k-anonymity and L diversity?

K-anonymity does not protect against such homogeneity and background knowledge attacks. Therefore, L-diversity proposes that there should be at least L different values for the sensitive attribute per combination of quasi-identifiers.