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.

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.

Abstract: Techniques for automatically selecting a differential privacy parameter in a neural network for data obfuscation are disclosed. In some embodiments, a computer system performs a method comprising: obtaining a privacy loss parameter of differential privacy; and training a neural network to perform data obfuscation operations, the training of the neural network comprising learning a variance parameter using the privacy loss parameter, the data obfuscation 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 and sampling data from the latent space distribution, the latent space distribution being based on the variance parameter; and decoding the sampled data of the latent space representation into output data.

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.

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.

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: 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.

Abstract: A client comprising a web browser is provided. The client is configured to: run an application in the web browser, the application comprising a sensor including sensor JavaScript code, wherein running the application comprises executing the sensor JavaScript code as the first JavaScript code in the web browser to activate the sensor; and wherein the sensor is configured to: gather data with respect to the application at runtime; and check predetermined application-specific rules against the gathered data for detecting client-side attacks at runtime.

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.

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.

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.

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.

Abstract: Various examples are directed to detecting anomalous modifications to a software component. For example, a computing device may receive, from a version control system, version metadata describing properties of a plurality of commits for the software component. The computing device may generate a plurality of commit clusters based, at least in part, on the properties of the plurality of commits. The computing device may determine a first anomalous commit of the plurality of commits and generate an alert message indicating a first code segment modified by the first commit.

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.

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.

Abstract: Methods, systems, and computer-readable storage media for optimizing query processing in encrypted databases. In some implementations, actions include receiving a query that is to be used to query an encrypted database, generating a plurality of query plans based on the query, each query plan including a local query and one or more remote queries, the local query being executable at a client-side and the one or more remote queries being executable at a server-side, selecting an optimal query plan from the plurality of query plans, providing one or more remote queries of the optimal query plan to the server-side for execution, receiving one or more remote results, and processing a local query of the optimal query plan and the one or more remote results to provide a final query result.

Abstract: A number of events are counted in different layers of a computing environment during execution of a software application. The number of counted events can be compared to a previously generated cluster set to determine that at least one of the counted events is an outlier. Data can then be provided that characterizes the at least one of the counted events determined to be an outlier. In some cases, some or all of the functionality of the software application can be selectively disabled. Related apparatus, systems, techniques and articles are also described.

Abstract: Various examples are directed to detecting anomalous modifications to a software component, For example, a computing device may receive, from a version control system, version metadata describing properties of a plurality of commits for the software component. The computing device may generate a plurality of commit clusters based, at least in part, on the properties of the plurality of commits. The computing device may determine a first anomalous commit of the plurality of commits and generate an alert message indicating a first code segment modified by the first commit.

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.

Abstract: A client comprising a web browser is provided. The client is configured to: run an application in the web browser, the application comprising a sensor including sensor JavaScript code, wherein running the application comprises executing the sensor JavaScript code as the first JavaScript code in the web browser to activate the sensor; and wherein the sensor is configured to: gather data with respect to the application at runtime; and check predetermined application-specific rules against the gathered data for detecting client-side attacks at runtime.