Rising User Privacy Against Predictive Context Awareness Through Adversarial Information Injection

Context-aware computing uses the wide range of information produced by mobile platforms to optimize the parameters of applications providing important services. However, as some of the data are either publicly exposed or can be acquired by malicious parties, a privacy issue arises. Recent studies extend this concept to context prediction, that is, the ability to forecast the future user context from current or past data. Whereas privacy in context-aware computing has been widely studied, the issue of impairing the ability to predict user context remains largely unexplored. This paper presents a framework based on Markov process theory to reduce the accuracy of predictions made by a malicious observer. Rather than attempting to hide current context, which is often purposely exposed by the user, the proposed methodology injects manufactured, and temporary, context updates to impair prediction. Numerical results from FourSquare databases demonstrate the privacy increase granted by the proposed technique and illustrate the tradeoff between user privacy and noise injection.