Abstract: Described herein is a method and system for providing privacy guarantees with an improved privacy-accuracy trade-off. Dynamic data can be accessed from a database. A sum model is selected from window sum, exponential decay sum, and polynomial decay sum. An algorithm is initiated that produces polylogarithmic bounded error in the range of a sum function associated with the selected sum model and independent of time steps. The data can be assembled in a dyadic tree structure. A non-linearity component can be added to nodes of the dyadic tree structure. For example, this can be a noise components or a weight applied to the update. This can be done, for example, to different nodes differently. Differential private estimators can be constructed for fixed steps of time. The differential private estimators can be applied to a query means or filtering system to enhance privacy protection from potential adversaries.

Abstract: A method and apparatus for ensuring a level of privacy for answering a convolution query on data stored in a database is provided. The method and apparatus includes the activities of determining (402) the level of privacy associated with at least a portion of the data stored in the database and receiving (404) query data, from a querier, for use in performing a convolution over the data stored in the database. The database is searched (406) for data related to the received query data and the data that corresponds to the received query data is retrieved (408) from the database. An amount of noise based on the determined privacy level is generated (410) and added (412) to the retrieved data to create noisy data which is then communicated (414) to the querier.