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This document discusses the vulnerability of user requests to query association attacks, where attackers can infer sensitive attributes. It presents two effective methods to mitigate these risks: many-to-one and many-to-many queries. Key techniques include k-anonymity, l-diversity, and t-closeness, which help ensure that user data is sufficiently obfuscated. These frameworks guarantee that users cannot be linked to fewer than ℓ distinct values of service attributes, enhancing privacy by making it difficult to track individual users based on location or service usage patterns.
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Query homogeneity attacks • Problem: • The attacker want to infer information from the sensitive attribute(s) involved in a user’s request => query association
Two methods to prevent query association: • Many-to-one queries: a k-anonymous cloaking region <-> single service attribute => k potential users who may be the owner of the service attribute • Many-to-many queries: a cloaking region <-> a set of service attribute values (ones belonging to the users inside the region)
l-diversity • Query l-diversity: ensures that a user cannot be linked to less than ℓ distinct service attribute values. equivalence class (EC)
t-closeness all disease is stomach problem
l-diversity & t-closeness • Satisfying location k-anonymity • Satisfying query ℓ-diversity • Satisfying query t-closeness query 3-diverse and location 3-anonymous
m-Invariance • Query m-Invariance: the number of possible query association attacks will increase if a user can be associated with more number of service attribute values.
