Individual privacy constraints on time-series data

Many recent applications depend on time series of data containing personal information. For example, the smart grid collects and distributes time series of energy-consumption data from households. Our concern is information hiding in such data according to individual privacy constraints, considering several constraints at a time. The existing information-hiding approaches we are aware of make limiting assumptions regarding the nature of such constraints. Our approach in turn lets the individuals concerned specify information that must be hidden arbitrarily, and it also lets the data receivers specify characteristics of the data needed to perform a certain task. We use these constraints to formulate an optimization problem that generates perturbed time series that fulfill the constraints of the data receivers and do not contain more sensitive information than allowed. Next, we propose a complexity-reduction approach that speeds up solving this optimization problem for time series by orders of magnitude. Three case studies on real-world data confirm that our approach is applicable to a wide range of application domains, and that it provides more protection against well-known privacy attacks such as re-identification, reconstruction and disaggregation. In addition, we provide a Java implementation of our approach and supplementary material on our web page.11http://www.ipd.kit.edu/laforetf/inpact HighlightsWe present a formalism for individual privacy constraints that is very general.Our solution subsumes strong privacy guarantees such as differential privacy.We identify solutions for any constraints using general optimization techniques.Our complexity-reduction approach speeds up computations by orders of magnitude.The evaluation shows that our solution protects well against known privacy attacks.