An electric vehicle charging control system may include an electric vehicle which extracts current time information, the integrity of which has been verified, by use of a public key cryptography, and charges power from a charging terminal, when it is determined that a charging contact certificate is valid, based on the extracted current time information, a server to transmit the current time information, the integrity of which has been verified using the public key cryptography, to the electric vehicle, and the charging terminal connected to the electric vehicle. The validity of the certificate used in vehicle charging and billing for charging is determined through the power line communication, and the security against the hacking is enhanced in the charging of the electric vehicle.

A method, system and devices for digital contact tracing security and privacy with proximity-based ID exchange with distance-bounding. The method is performed by a first wireless communication device and provides for exchanging IDs with a second wireless communication device. A rolling proximity identifier A associated with the first wireless communication device is sent to the second wireless communication device. A rolling proximity identifier B associated with the second wireless communication device is received from the second wireless communication device. A cryptographic challenge response authentication with time-based distance-bounding is performed based on a hash value determined from the rolling proximity identifiers in accordance with a hash function. The rolling proximity identifier of the second wireless communication device is only stored in memory in response to a successful cryptographic challenge response authentication.

Method for encoding/decoding a signal at a first and second communication node (N1; N2) in a road vehicle. A signal (1) from an on-board sensor (10) is encoded using a first encoding scheme (a), encoding the formed single encoded sensor signal (1a) using a second encoding scheme (b), decoding this double encoded sensor signal (1ab) in the second communication node (N2) based on the second encoding scheme (b), forming a decoded single encoded sensor signal (1a′). In the first communication node (N2), performing a comparison analysis, comprising at least one of the following: comparing the decoded single encoded sensor signal (1a′) with a stored single encoded sensor signal (1a), or after encoding the decoded single encoded sensor signal (1a′) with the second encoding scheme (b) comparing (110) the thus formed double encoded sensor signal (1a′b) with a stored double encoded sensor signal (1ab). If the compared sensor signals (1a′,1a; 1 ab,1a′b) match, then sending (111) a signal to the second communication node (N2) validating the sensor signal (1), and if they do not match, then initiating (112) a corrective action.

This invention relates to personal identity management and verifiable and authenticable methods and systems for mobile personal credentials. A critical problem is knowing the true identity of counterparties while using electronic messaging or conducting online transactions. Existing security measures can be bypassed when identity is presented in electronic form. The inventors address these issues by providing digital ID document in conjunction with data that permits the other party to verify ID. Further, the inventors either link the electronic ID to its physical counterpart or to the actual physical individual presenting the ID. Immutable digital ledger technology, such as blockchain, is used to provide trustworthy authentication of digital identity along with assurance that the identity presented belongs to the individual presenting it.

This detection device detects an attack in an on-vehicle network that includes a bus in which a frame including identification information that allows recognition of at least one of a transmission source and a destination is transmitted. In the bus, a plurality of the frames including pieces of the identification information different from each other are transmitted. The detection device includes: a monitoring unit configured to monitor a communication error in the bus; an aggregation unit configured to aggregate a communication error occurrence state regarding each piece of the identification information on the basis of a monitoring result by the monitoring unit; and a detection unit configured to detect the attack on the basis of an aggregation result by the aggregation unit.

An autonomous driving control method for a vehicle includes: converting a driving mode into a restricted area autonomous driving mode in which memory access, communication with a network, and information acquisition are restricted in a restricted area; transmitting a destination in the restricted area and an authentication key to a server by an autonomous driving system; checking validity of the authentication key, and generating a global path to the destination in the restricted area when the authentication key is valid, by the server; encrypting the global path and transmitting it with a decryption key to the autonomous driving system by the server; and restoring the encrypted global path using the decryption key by the autonomous driving system. Autonomous vehicles of the present disclosure may be associated with artificial intelligence modules, drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G service, etc.

An onboard device transfers an encrypted message encrypted outside a vehicle to one or more vehicle controllers connected to a vehicle network. When the encrypted message is an individual message to one of the vehicle controllers, the onboard device transmits the encrypted message to the one of the vehicle controllers via the vehicle network. When the encrypted message is a common message to the one or more vehicle controllers, the onboard device decrypts the encrypted message using an encryption key owned by the onboard device and then transmits the decrypted message to the one or more vehicle controllers via the vehicle network.

Disclosed herein are techniques for identifying software dependencies based on functional line-of-code behavior and relation models. Techniques include accessing a first line-of-code behavior and relation model representing execution of functions of a first portion of executable code, the first portion of executable code being associated with a first symbol; detecting a change to the first portion of executable code; constructing, based on the changed first portion of executable code, a second line-of-code behavior and relation model representing execution of functions of the changed first portion of executable code; determining, based on the constructed second model, a dependency between (i) the changed first portion of executable code or the first symbol and (ii) a second symbol; and generating, based on the determined difference, a report identifying the dependency.

Generating, by a computing device, a device secret, the generating comprising: providing, by at least one physical unclonable function (PUF), at least one value; and generating, using a key derivative function (KDF), the device secret, wherein the at least one value provided by the at least one PUF is an input to the KDF; and storing, in memory of the computing device, the generated device secret.

An example operation may include one or more of receiving, from at least one sensor associated with a transport, severity of damage information related to the transport, when the severity of damage exceeds a threshold: identifying sensitive data on the transport; removing a portion of the sensitive data from the transport; storing the removed portion on a storage apart from the transport; identifying additional data to replace the removed portion of the sensitive data; and adding the identified additional data to the sensitive data on the transport.