Abstract: A method of determining a range between two devices, which can for instance be wireless devices, such as handheld devices. The method performs a double-sided two-way ranging protocol at the two devices. This protocol causes the two devices to transmit and receive signals, with a view to determining the range between the two devices, using a two-way ranging methods, where each of the signals is a composite tone generated as a composition of two waveforms that are timewise separated by a gap to form a bipolar waveform. The gap is a zero or low-amplitude signal, contrasting with the two waveforms. The particular pattern in the correlation footprint eases the determination of the time of flight. The method can be used to measure social distancing.

Abstract: An encoder includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generate a key, estimate a network capacity, and encode each bit of the key using a random matrix of a selected rank and the estimated network capacity for secure transmission of the key through a network.

Abstract: A method of determining a range between two devices, which can for instance be wireless devices, such as handheld devices. The method performs a double-sided two-way ranging protocol at the two devices. This protocol causes the two devices to transmit and receive signals, with a view to determining the range between the two devices, using a two-way ranging methods, where each of the signals is a composite tone generated as a composition of two waveforms that are timewise separated by a gap to form a bipolar waveform. The gap is a zero or low-amplitude signal, contrasting with the two waveforms. The particular pattern in the correlation footprint eases the determination of the time of flight. The method can be used to measure social distancing.

Abstract: An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to construct an encoded message using a message and a random element, construct a hash using a shared secret, and transmit the encoded message and the hash to a destination, through a network.

Abstract: An example operation may include one or more of extracting a data object from a machine-readable code, where the data object comprises fields of data signed with a digital signature of a private key, detecting an identifier of an issuer of the data object from the extracted data object, retrieving a public key from a blockchain based on the identifier of the issuer detected from the field of the extracted data object, and verifying the digital signature of the private key based on the fetched public key

Abstract: A decoder deployed in one or more terminals, includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to receiving a noisy message and a noisy hash from the network, searching for a pair of matching candidates for the hash and message from two row spaces of noisy message vectors using a shared secret with an encoder, and outputting, by the decoder, a decoded message if the searching is successful.

Abstract: An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to construct an encoded message using a message and a random element, construct a hash using a shared secret, and transmit the encoded message and the hash to a destination, through a network.

Abstract: An encoder includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generate a key, estimate a network capacity, and encode each bit of the key using a random matrix of a selected rank and the estimated network capacity for secure transmission of the key through a network.

Abstract: An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generating a message by aggregating a plurality of incoming packets, constructing an encoded message using the message and a random matrix, constructing of a hash using a shared secret, and transmitting the encoded message and the hash to a destination, through a network that performs network coding operations.

Abstract: An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generate a k-bit key, where k is a positive integer, estimate an upper bound of a number of eavesdropped links, encode each bit of the k-bit key using a random matrix of a selected rank, and transmit the encoded k-bit key through a network that performs linear operations on packets.

Abstract: The present principles are directed to identifying and classifying web traffic inside encrypted network tunnels. A method includes analyzing network traffic of unencrypted data packets to detect packet traffic, timing, and size patterns. The detected packet, timing, and size traffic patterns are correlated to at least a packet destination and a packet source of the unencrypted data packets to create at least one of a training corpus and a model built from the training corpus. The at least one of the corpus and model is stored in a memory device. Packet traffic, timing, and size patterns of encrypted data packets are observed. The observed packet traffic, timing, and size patterns of the encrypted data packets are compared to at least one of the training corpus and the model to classify the encrypted data packets with respect to at least one of a predicted network host and predicted path information.

Abstract: The present principles are directed to identifying and classifying web traffic inside encrypted network tunnels. A method includes analyzing network traffic of unencrypted data packets to detect packet traffic, timing, and size patterns. The detected packet, timing, and size traffic patterns are correlated to at least a packet destination and a packet source of the unencrypted data packets to create at least one of a training corpus and a model built from the training corpus. The at least one of the corpus and model is stored in a memory device. Packet traffic, timing, and size patterns of encrypted data packets are observed. The observed packet traffic, timing, and size patterns of the encrypted data packets are compared to at least one of the training corpus and the model to classify the encrypted data packets with respect to at least one of a predicted network host and predicted path information.

Abstract: The present principles are directed to identifying and classifying web traffic inside encrypted network tunnels. A method includes analyzing network traffic of unencrypted data packets to detect packet traffic, timing, and size patterns. The detected packet, timing, and size traffic patterns are correlated to at least a packet destination and a packet source of the unencrypted data packets to create at least one of a training corpus and a model built from the training corpus. The at least one of the corpus and model is stored in a memory device. Packet traffic, timing, and size patterns of encrypted data packets are observed. The observed packet traffic, timing, and size patterns of the encrypted data packets are compared to at least one of the training corpus and the model to classify the encrypted data packets with respect to at least one of a predicted network host and predicted path information.

Abstract: An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generate a k-bit key, where k is a positive integer, estimate an upper bound of a number of eavesdropped links, encode each bit of the k-bit key using a random matrix of a selected rank, and transmit the encoded k-bit key through a network that performs linear operations on packets.

Abstract: An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generating a message by aggregating a plurality of incoming packets, constructing an encoded message using the message and a random matrix, constructing of a hash using a shared secret, and transmitting the encoded message and the hash to a destination, through a network that performs network coding operations.

Abstract: A decoder deployed in one or more terminals, includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to receiving a noisy message and a noisy hash from the network, searching for a pair of matching candidates for the hash and message from two row spaces of noisy message vectors using a shared secret with an encoder, and outputting, by the decoder, a decoded message if the searching is successful.

Abstract: The present principles are directed to identifying and classifying web traffic inside encrypted network tunnels. A method includes analyzing network traffic of unencrypted data packets to detect packet traffic, timing, and size patterns. The detected packet, timing, and size traffic patterns are correlated to at least a packet destination and a packet source of the unencrypted data packets to create at least one of a training corpus and a model built from the training corpus. The at least one of the corpus and model is stored in a memory device. Packet traffic, timing, and size patterns of encrypted data packets are observed. The observed packet traffic, timing, and size patterns of the encrypted data packets are compared to at least one of the training corpus and the model to classify the encrypted data packets with respect to at least one of a predicted network host and predicted path information.

Abstract: The present principles are directed to identifying and classifying web traffic inside encrypted network tunnels. A method includes analyzing network traffic of unencrypted data packets to detect packet traffic, timing, and size patterns. The detected packet, timing, and size traffic patterns are correlated to at least a packet destination and a packet source of the unencrypted data packets to create at least one of a training corpus and a model built from the training corpus. The at least one of the corpus and model is stored in a memory device. Packet traffic, timing, and size patterns of encrypted data packets are observed. The observed packet traffic, timing, and size patterns of the encrypted data packets are compared to at least one of the training corpus and the model to classify the encrypted data packets with respect to at least one of a predicted network host and predicted path information.

Abstract: The present principles are directed to identifying and classifying web traffic inside encrypted network tunnels. A method includes analyzing network traffic of unencrypted data packets to detect packet traffic, timing, and size patterns. The detected packet, timing, and size traffic patterns are correlated to at least a packet destination and a packet source of the unencrypted data packets to create at least one of a training corpus and a model built from the training corpus. The at least one of the corpus and model is stored in a memory device. Packet traffic, timing, and size patterns of encrypted data packets are observed. The observed packet traffic, timing, and size patterns of the encrypted data packets are compared to at least one of the training corpus and the model to classify the encrypted data packets with respect to at least one of a predicted network host and predicted path information.

Abstract: The present principles are directed to identifying and classifying web traffic inside encrypted network tunnels. A method includes analyzing network traffic of unencrypted data packets to detect packet traffic, timing, and size patterns. The detected packet, timing, and size traffic patterns are correlated to at least a packet destination and a packet source of the unencrypted data packets to create at least one of a training corpus and a model built from the training corpus. The at least one of the corpus and model is stored in a memory device. Packet traffic, timing, and size patterns of encrypted data packets are observed. The observed packet traffic, timing, and size patterns of the encrypted data packets are compared to at least one of the training corpus and the model to classify the encrypted data packets with respect to at least one of a predicted network host and predicted path information.