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In this paper, localized information privacy (LIP) is proposed, as a new privacy definition, which allows statistical aggregation while protecting users' privacy without relying on a trusted third party. The notion of context-awareness is incorporated in LIP by the introduction of priors, which enables the design of privacy-preserving data aggregation with knowledge of priors. We show that LIP relaxes the Localized Differential Privacy (LDP) notion by explicitly modeling the adversary's knowledge. However, it is stricter than 2ǫ-LDP and ǫ-mutual information privacy. The incorporation of local priors allows LIP to achieve higher utility compared to other approaches. For four different applications in privacy-preserving data aggregation, including survey, summation, weighted summation and histogram, we present an optimization framework, with the goal of minimizing the mean square error while satisfying the LIP privacy constraints. Utility-privacy tradeoffs are obtained under each model in closed-form, we then theoretically compare with the centralized information privacy and LDP. At last, we validate our analysis by simulations using both synthetic and real-world data. Results show that our LIP mechanism provides better utility-privacy tradeoffs than LDP and when the prior is not uniformly distributed, the advantage of LIP is even more significant.

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