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公开(公告)号：US12001588B2
公开(公告)日：2024-06-04
申请号：US17086244
申请日：2020-10-30
Applicant: SAP SE
Inventor： Daniel Bernau , Philip-William Grassal , Hannah Keller , Martin Haerterich
IPC: G06F21/62 , G06F17/18 , G06F18/214 , G06N20/00
CPC classification number: G06F21/6254 , G06F17/18 , G06F18/2148 , G06N20/00
Abstract: Data is received that specifies a bound for an adversarial posterior belief ρc that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private function output. Privacy parameters ε, δ 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 ρc 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.
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公开(公告)号：US20220138348A1
公开(公告)日：2022-05-05
申请号：US17086244
申请日：2020-10-30
Applicant: SAP SE
Inventor： Daniel Bernau , Philip-William Grassal , Hannah Keller , Martin Haerterich
Abstract: Data is received that specifies a bound for an adversarial posterior belief ρc that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private function output. Privacy parameters ε, δ 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 ρc 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.
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Patent Agency Ranking
公开(公告)号：US12147577B2
公开(公告)日：2024-11-19
申请号：US18581254
申请日：2024-02-19
Applicant: SAP SE
Inventor： Daniel Bernau , Philip-William Grassal , Hannah Keller , Martin Haerterich
IPC: G06F21/62 , G06F17/18 , G06F18/214 , G06N20/00
Abstract: Data is received that specifies a bound for an adversarial posterior belief ρc that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private function output. Privacy parameters ε, δ 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 ρc 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.
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公开(公告)号：US20250036811A1
公开(公告)日：2025-01-30
申请号：US18904462
申请日：2024-10-02
Applicant: SAP SE
Inventor： Daniel Bernau , Philip-William Grassal , Hannah Keller , Martin Haerterich
IPC: G06F21/62 , G06F17/18 , G06F18/214 , G06N20/00
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 ε, δ 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.
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公开(公告)号：US20240211635A1
公开(公告)日：2024-06-27
申请号：US18581254
申请日：2024-02-19
Applicant: SAP SE
Inventor： Daniel Bernau , Philip-William Grassal , Hannah Keller , Martin Haerterich
IPC: G06F21/62 , G06F17/18 , G06F18/214 , G06N20/00
CPC classification number: G06F21/6254 , G06F17/18 , G06F18/2148 , G06N20/00
Abstract: Data is received that specifies a bound for an adversarial posterior belief ρc that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private function output. Privacy parameters ε, δ 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 ρc 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.
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公开(公告)号：US11449639B2
公开(公告)日：2022-09-20
申请号：US16442336
申请日：2019-06-14
Applicant: SAP SE
Inventor： Daniel Bernau , Jonas Robl , Philip-William Grassal , Florian Kerschbaum
Abstract: Machine learning model data privacy can be maintained by training a machine learning model forming part of a data science process using data anonymized using each of two or more differential privacy mechanisms. Thereafter, it is determined, for each of the two or more differential privacy mechanisms, a level of accuracy and a level precision when evaluating data with known classifications. Subsequently, using the respective determined levels of precision and accuracy, a mitigation efficiency ratio is determined for each of the two or more differential privacy mechanisms. The differential privacy mechanism having a highest mitigation efficiency ratio is then incorporated into the data science process. Related apparatus, systems, techniques and articles are also described.
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公开(公告)号：US10746567B1
公开(公告)日：2020-08-18
申请号：US16361405
申请日：2019-03-22
Applicant: SAP SE
Inventor： Daniel Bernau , Philip-William Grassal , Florian Kerschbaum
Abstract: Methods, systems, and computer-readable storage media for privacy preserving metering is described herein. A resource threshold value associated with anonymizing meter data for resources metered at a first destination is received. Based on a noise scale value and the resource threshold value, an individual inference value of the first destination is computed. The individual inference value defines a probability of distinguishing the first destination as a contributor to a query result based on anonymized meter data of the first destination and other destinations according to the noise scale value. The noise scale value is defined for a processing application. Based on evaluating the individual inference value, it is determined to provide anonymized meter data for metered resources at the first destination. An activation of a communication channel for providing the anonymized meter data for metered resources is triggered. The communication channel is between the first destination and the processing application.
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