Abstract: Some embodiments employ a novel procedure of training an artificial intelligence system (e.g., set of deep neural networks) for anomaly detection in applications such as natural language processing and computer security. Token sequences selected from a training corpus are distorted according to at least one of a plurality of pre-determined sequence transformations, before being fed to a sequence analyzer. In turn, the sequence analyzer is trained to correctly guess which transformation was used to generate the respective input token sequence.

Abstract: Described systems and methods allow carrying out privacy-preserving DNS exchanges. In some embodiments, a client machine engages in a private information retrieval (PIR) exchange with a nameserver. In response to receiving an encrypted query from the client, the query formulated according to a domain name, the nameserver may extract a record (e.g., an IP address) from a domain name database without decrypting the respective query. Some embodiments achieve such information retrieval by the use of homomorphic encryption.

Abstract: Some embodiments employ a consensus-building procedure to train a multitask graph comprising a plurality of nodes interconnected by a plurality of edges, wherein each node is associated with a task of determining a set of node-specific attributes of a set of input data, and each edge comprises an AI module (e.g., neural network) configured to determine attributes of an end node according to attributes of a start node of the respective edge. Training fosters consensus between all edges converging to a node. The trained multitask graph may then be deployed in a threat detector configured to determine whether an input set of data is indicative of malice (e.g., malware, intrusion, online threat, etc.).

Abstract: Some embodiments enable distributing data (e.g., recorded video, photographs, recorded audio, etc.) to a plurality of users in a manner which preserves the privacy of the respective users. Some embodiments leverage homomorphic encryption and proxy re-encryption techniques to manipulate the respective data so that selected portions of it are revealed according to an identity of the user currently accessing the respective data.

Abstract: Described systems and methods allow carrying out privacy-preserving DNS exchanges. In some embodiments, a client machine engages in a private information retrieval (PIR) exchange with a nameserver. In response to receiving an encrypted query from the client, the query formulated according to a domain name, the nameserver may extract a record (e.g., an IP address) from a domain name database without decrypting the respective query. Some embodiments achieve such information retrieval by the use of homomorphic encryption.

Abstract: Some embodiments enable distributing data (e.g., recorded video, photographs, recorded audio, etc.) to a plurality of users in a manner which preserves the privacy of the respective users. Some embodiments leverage homomorphic encryption and proxy re-encryption techniques to manipulate the respective data so that selected portions of it are revealed according to an identity of the user currently accessing the respective data.

Abstract: Some embodiments enable distributing data (e.g., recorded video, photographs, recorded audio, etc.) to a plurality of users in a manner which preserves the privacy of the respective users. Some embodiments leverage homomorphic encryption and proxy re-encryption techniques to manipulate the respective data so that selected portions of it are revealed according to an identity of the user currently accessing the respective data.

Abstract: Some embodiments enable distributing data (e.g., recorded video, photographs, recorded audio, etc.) to a plurality of users in a manner which preserves the privacy of the respective users. Some embodiments leverage homomorphic encryption and proxy re-encryption techniques to manipulate the respective data so that selected portions of it are revealed according to an identity of the user currently accessing the respective data.

Abstract: Some embodiments employ a novel procedure of training an artificial intelligence system (e.g., set of deep neural networks) for anomaly detection in applications such as natural language processing and computer security. Token sequences selected from a training corpus are distorted according to at least one of a plurality of pre-determined sequence transformations, before being fed to a sequence analyzer. In turn, the sequence analyzer is trained to correctly guess which transformation was used to generate the respective input token sequence.

Abstract: Described systems and methods allow carrying out privacy-preserving DNS exchanges. In some embodiments, a client machine engages in a private information retrieval (PIR) exchange with a nameserver. In response to receiving an encrypted query from the client, the query formulated according to a domain name, the nameserver may extract a record (e.g., an IP address) from a domain name database without decrypting the respective query. Some embodiments achieve such information retrieval by the use of homomorphic encryption.

Abstract: Some embodiments enable distributing data (e.g., recorded video, photographs, recorded audio, etc.) to a plurality of users in a manner which preserves the privacy of the respective users. Some embodiments leverage homomorphic encryption and proxy re-encryption techniques to manipulate the respective data so that selected portions of it are revealed according to an identity of the user currently accessing the respective data.

Abstract: Some embodiments enable distributing data (e.g., recorded video, photographs, recorded audio, etc.) to a plurality of users in a manner which preserves the privacy of the respective users. Some embodiments leverage homomorphic encryption and proxy re-encryption techniques to manipulate the respective data so that selected portions of it are revealed according to an identity of the user currently accessing the respective data.

Abstract: Described systems and methods allow an automatic translation from a natural language (e.g., English) into an artificial language such as a structured query language (SQL). In some embodiments, a translator module includes an encoder component and a decoder component, both components comprising recurrent neural networks. Training the translator module comprises two stages. A first stage trains the translator module to produce artificial language (AL) output when presented with an AL input. For instance, the translator is first trained to reproduce an AL input. A second stage of training comprises training the translator to produce AL output when presented with a natural language (NL) input.

Abstract: Described systems and methods allow an automatic translation from a natural language (e.g., English) into an artificial language such as a structured query language (SQL). In some embodiments, a translator module includes an encoder component and a decoder component, both components comprising recurrent neural networks. Training the translator module comprises two stages. A first stage trains the translator module to produce artificial language (AL) output when presented with an AL input. For instance, the translator is first trained to reproduce an AL input. A second stage of training comprises training the translator to produce AL output when presented with a natural language (NL) input.

Abstract: Described systems and methods allow an automatic translation from a natural language (e.g., English) into an artificial language such as a structured query language (SQL). In some embodiments, a translator module includes an encoder component and a decoder component, both components comprising recurrent neural networks. Training the translator module comprises two stages. A first stage trains the translator module to produce artificial language (AL) output when presented with an AL input. For instance, the translator is first trained to reproduce an AL input. A second stage of training comprises training the translator to produce AL output when presented with a natural language (NL) input.

Abstract: Described systems and methods allow an automatic translation from a natural language (e.g., English) into an artificial language such as a structured query language (SQL). In some embodiments, a translator module includes an encoder component and a decoder component, both components comprising recurrent neural networks. Training the translator module comprises two stages. A first stage trains the translator module to produce artificial language (AL) output when presented with an AL input. For instance, the translator is first trained to reproduce an AL input. A second stage of training comprises training the translator to produce AL output when presented with a natural language (NL) input.

Abstract: Described systems and methods allow a mobile device, such as a smartphone or a tablet computer, to protect a user of the respective device from fraud and/or loss of privacy. In some embodiments, the mobile device receives from a server a risk indicator indicative of whether executing a target application causes a privacy risk. Determining the risk indicator includes automatically supplying a test input to a data field used by the target application, the data field configured to hold a private item such as a password or a geolocation indicator. Determining the risk indicator further comprises determining whether a test device executing an instance of the target application transmits an indicator of the test input, such as the test input itself or a hash of the test input, to another party on the network.

Abstract: Described systems and methods allow a mobile device, such as a smartphone or a tablet computer, to protect a user of the respective device from fraud and/or loss of privacy. In some embodiments, the mobile device receives from a server a risk indicator indicative of whether executing a target application causes a privacy risk. Determining the risk indicator includes automatically supplying a test input to a data field used by the target application, the data field configured to hold a private item such as a password or a geolocation indicator. Determining the risk indicator further comprises determining whether a test device executing an instance of the target application transmits an indicator of the test input, such as the test input itself or a hash of the test input, to another party on the network.

Abstract: Described systems and methods allow a mobile device, such as a smartphone or a tablet computer, to protect a user of the respective device from fraud and/or loss of privacy. In some embodiments, the mobile device receives from a server a risk indicator indicative of whether executing a target application causes a privacy risk. Determining the risk indicator includes automatically supplying a test input to a data field used by the target application, the data field configured to hold a private item such as a password or a geolocation indicator. Determining the risk indicator further comprises determining whether a test device executing an instance of the target application transmits an indicator of the test input, such as the test input itself or a hash of the test input, to another party on the network.