Abstract: Data is received that specifies a bound for an adversarial posterior belief pc that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private function output. Privacy parameters ?, ? are then calculated based on the received data that govern a differential privacy (DP) algorithm to be applied to a function to be evaluated over a dataset. The calculating is based on a ratio of probabilities distributions of different observations, which are bound by the posterior belief pc as applied to a dataset. The calculated privacy parameters are then used to apply the DP algorithm to the function over the dataset. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that specifies a bound for an adversarial posterior belief ?c that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private function output. Privacy parameters ?, ? are then calculated based on the received data that govern a differential privacy (DP) algorithm to be applied to a function to be evaluated over a dataset. The calculating is based on a ratio of probabilities distributions of different observations, which are bound by the posterior belief ?c as applied to a dataset. The calculated privacy parameters are then used to apply the DP algorithm to the function over the dataset. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that specifies a bound for an adversarial posterior belief ?c that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private function output. Privacy parameters ?, ? are then calculated based on the received data that govern a differential privacy (DP) algorithm to be applied to a function to be evaluated over a dataset. The calculating is based on a ratio of probabilities distributions of different observations, which are bound by the posterior belief ?c as applied to a dataset. The calculated privacy parameters are then used to apply the DP algorithm to the function over the dataset. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that specifies a bound for an adversarial posterior belief ?c that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private function output. Privacy parameters ?, ? are then calculated based on the received data that govern a differential privacy (DP) algorithm to be applied to a function to be evaluated over a dataset. The calculating is based on a ratio of probabilities distributions of different observations, which are bound by the posterior belief ?c as applied to a dataset. The calculated privacy parameters are then used to apply the DP algorithm to the function over the dataset. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that specifies a bound for an adversarial posterior belief ?c that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private function output. Privacy parameters ?, ? are then calculated based on the received data that govern a differential privacy (DP) algorithm to be applied to a function to be evaluated over a dataset. The calculating is based on a ratio of probabilities distributions of different observations, which are bound by the posterior belief ?c as applied to a dataset. The calculated privacy parameters are then used to apply the DP algorithm to the function over the dataset. Related apparatus, systems, techniques and articles are also described.