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公开(公告)号：US20200097763A1
公开(公告)日：2020-03-26
申请号：US16140022
申请日：2018-09-24
Applicant: SAP SE
Inventor： Martin Haerterich , Benjamin Hilprecht , Daniel Bernau
Abstract: Various examples are directed to systems and methods for detecting training data for a generative model. A computer system may access generative model sample data and a first test sample. The computer system may determine whether a first generative model sample of the plurality of generative model samples is within a threshold distance of the first test sample and whether a second generative model sample of the plurality of generative model samples is within the threshold distance of the first test sample. The computer system may determine that a probability that the generative model was trained with the first test sample is greater than or equal to a threshold probability based at least in part on whether the first generative model sample is within the threshold distance of the first test sample, the determining also based at least in part on whether the second generative model sample is within the threshold distance of the first test sample.
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公开(公告)号：US10454969B2
公开(公告)日：2019-10-22
申请号：US15650974
申请日：2017-07-17
Applicant: SAP SE
Inventor： Martin Haerterich , Martin Johns , Marius Musch
Abstract: Various embodiments of systems, computer program products, and methods to automatically generate low-interaction honeypots to protect application landscapes through are described herein. In an aspect, representative applications associated with resources in a network are identified. The low-interaction honeypots are automatically generated for the identified representative applications. Further, the representative applications are probed to retrieve responses corresponding to different requests. Templates are generated corresponding to request-response pairs by parsing the responses and the requests. During operation, new requests for accessing the resources are responded based on the generated templates. The new requests and corresponding responses are recorded.
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公开(公告)号：US10242180B2
公开(公告)日：2019-03-26
申请号：US15403603
申请日：2017-01-11
Applicant: SAP SE
Inventor： Christoph Haefner , Martin Johns , Martin Haerterich
Abstract: Systems and methods are provided herein for establishing a protection framework for a component. Identified assets of a component requiring protection from a potential attack are received. A list of assets is generated based on the identified assets. A protection framework is configured to include at least one defensive pattern to protect the list of assets against the potential attack. The protection framework is executed to establish a hardened boundary between the component and an attack surface of the component.
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4.发明申请公开(公告)号：US20180198810A1
公开(公告)日：2018-07-12
申请号：US15401861
申请日：2017-01-09
Applicant: SAP SE
Inventor： Martin Haerterich
CPC classification number: H04L63/1425 , G06N20/00 , H04L63/168
Abstract: A sequence of events by a single user with at least one computing system are monitored. Each event characterizes user interaction with the at least one computing system and the sequence of events form a plurality of pairwise disjoint log samples. Thereafter, it is determined, using an adjacency graph trained using a plurality of log samples generated by a plurality of users, whether any of the log samples is anomalous. Data can be provided that characterizes the log samples determined to be anomalous. Related apparatus, systems, techniques and articles are also described.
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公开(公告)号：US12105847B2
公开(公告)日：2024-10-01
申请号：US17550634
申请日：2021-12-14
Applicant: SAP SE
Inventor： Benjamin Weggenmann , Martin Haerterich , Florian Knoerzer
IPC: G06F21/62
CPC classification number: G06F21/6254
Abstract: Techniques for implementing a differentially private variational autoencoder for data obfuscation are disclosed. In some embodiments, a computer system performs operations comprising: encoding input data into a latent space representation of the input data, the encoding of the input data comprising: inferring latent space parameters of a latent space distribution based on the input data, the latent space parameters comprising a mean and a standard deviation, the inferring of the latent space parameters comprising bounding the mean within a finite space and using a global value for the standard deviation, the global value being independent of the input data; and sampling data from the latent space distribution; and decoding the sampled data of the latent space representation into output data.
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Patent Agency Ranking
公开(公告)号：US20230376626A1
公开(公告)日：2023-11-23
申请号：US17751397
申请日：2022-05-23
Applicant: SAP SE
Inventor： Martin Haerterich , Benjamin Weggenmann
IPC: G06F21/62
CPC classification number: G06F21/6245
Abstract: Various examples are directed to systems and methods for obscuring private information in input data. A system may apply an encoder model to an input data unit to generate a latent space representation of the input data unit. The system may apply multi-dimensional noise to the latent space representation of the input data unit, the multi-dimensional noise having a first value in a first latent space dimension and a second value different than the first value in a second latent space dimension. The system may apply a decoder model to the latent space representation of the input data unit to generate an obscured data unit.
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7.发明授权公开(公告)号：US11501172B2
公开(公告)日：2022-11-15
申请号：US16219645
申请日：2018-12-13
Applicant: SAP SE
Inventor： Benjamin Hilprecht , Daniel Bernau , Martin Haerterich
Abstract: A system is described that can include a machine learning model and at least one programmable processor communicatively coupled to the machine learning model. The machine learning model can receive data, generate a continuous probability distribution associated with the data, sample a latent variable from the continuous probability distribution to generate a plurality of samples, and generate reconstructed data from the plurality of samples. The at least one programmable processor can compute a reconstruction error by determining a distance between the reconstructed data and the data, and generate, based on the reconstruction error, an indication representing whether a specific record within the received data was used to train the machine learning model. Related apparatuses, methods, techniques, non-transitory computer programmable products, non-transitory machine-readable medium, articles, and other systems are also within the scope of this disclosure.
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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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公开(公告)号：US20220070150A1
公开(公告)日：2022-03-03
申请号：US17010501
申请日：2020-09-02
Applicant: SAP SE
Inventor： Martin Haerterich , Benjamin Weggenmann , Florian Knoerzer
Abstract: Various examples are directed to systems and methods for obscuring personal information in a sensor data stream. A system may apply an encoder model to the sensor data stream to generate a latent space representation of the sensor data stream. The system may also apply a noise-scaling parameter to the latent space representation of the sensor data stream and apply a decoder model to the latent space representation of the sensor data stream to generate an obscured data stream.
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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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