Abstract: A method includes receiving a first message that includes a first relational key element based on a first group element, and a second relational key element based on the first group element and raised to the power of a first plaintext value. The method also includes receiving a second message that includes a third relational key element based on a second group element, and a fourth relational key element based on the second group element and raised to the power of a second plaintext value. The method additionally includes comparing the first message to the second message without decryption of the first or second messages and, based on the comparison, determining that the first plaintext value and the second plaintext value are the same.

Abstract: A method includes receiving biometric data, the biometric data non-uniformly distributed and processing the biometric data to a level of randomness as a plaintext vector, the level of randomness associated with a security level. The method also includes encrypting the plaintext vector using a relational linearity encryption scheme to generate a linearity ciphertext representative of the plaintext vector, encrypting the plaintext vector using a relational proximity encryption scheme to generate a proximity ciphertext representative of the plaintext vector, and communicating the linearity ciphertext and the proximity ciphertext to an authentication server.

Abstract: A method includes receiving a first message that includes a first relational key element based on a first group element, and a second relational key element based on the first group element and raised to the power of a first plaintext value. The method also includes receiving a second message that includes a third relational key element based on a second group element, and a fourth relational key element based on the second group element and raised to the power of a second plaintext value. The method additionally includes comparing the first message to the second message without decryption of the first or second messages and, based on the comparison, determining that the first plaintext value and the second plaintext value are the same.

Abstract: A method and apparatus for improving and performing mmW beam tracking is disclosed. Localization methods to improve prediction of the position of a WTRU are described, which may allow a millimeter wave base station (mB) to appropriately select a modified beam and to perform more efficient handover. WTRUs may report directional signal strength measurements to mBs, which may then be used to generate a directional radio environment map (DREM) for use in identifying secondary links to use when a primary link fails. Additional localization techniques using internal/external information for prediction are described. Historical data use and the use of data obtained from mB-mB cooperation including feedback information and reference signaling information are also described. Methods for beam tracking for directional relays and initial beam training optimization are described as well.

Abstract: A method includes receiving a first and a second linearity ciphertexts representative of a first and second biometric templates, respectively that are encrypted using a relational linearity encryption scheme (linearity scheme). The linearity scheme is based on learning parity with noise. The method includes discovering a linearity relationship between the first and the second linearity ciphertexts. The method includes receiving a first and a second proximity hash value representative of the first and second biometric templates, respectively encrypted using a relational proximity hash scheme (proximity scheme). The proximity scheme is based on the linearity scheme and an error correcting code. The method includes detecting a proximity between the first and the second proximity hash value in terms of a Hamming distance. The method includes authenticating an identity of a user based on the proximity and the linearity relationship.

Abstract: Methods and apparatus may perform dual-band or multi-band mesh operations. A dual-band mesh station (MSTA) capable of operating in an O-band and a D-band may seek to join a mesh network, and may receive O-band beacons from at least one MSTA in the mesh network, where the O-band beacons may include D-band mesh information. The joining MSTA may transmit D-band beacons in a time-period specified by the O-band beacon, and on a condition that a beacon response message is received, may further transmit D-band association information via O-band management frames to join mesh network on the D-band. The joining MSTA may perform contention-free scheduled access in the D-band while sharing D-band transmission information in the O-band to enable concurrent communication in the D-band by neighboring multi-band MSTAs.

Abstract: A method includes receiving a first and a second linearity ciphertexts representative of a first and second biometric templates, respectively that are encrypted using a relational linearity encryption scheme (linearity scheme). The linearity scheme is based on learning parity with noise. The method includes discovering a linearity relationship between the first and the second linearity ciphertexts. The method includes receiving a first and a second proximity hash value representative of the first and second biometric templates, respectively encrypted using a relational proximity hash scheme (proximity scheme). The proximity scheme is based on the linearity scheme and an error correcting code. The method includes detecting a proximity between the first and the second proximity hash value in terms of a Hamming distance. The method includes authenticating an identity of a user based on the proximity and the linearity relationship.

Abstract: A method includes receiving biometric data, the biometric data non-uniformly distributed and processing the biometric data to a level of randomness as a plaintext vector, the level of randomness associated with a security level. The method also includes encrypting the plaintext vector using a relational linearity encryption scheme to generate a linearity ciphertext representative of the plaintext vector, encrypting the plaintext vector using a relational proximity encryption scheme to generate a proximity ciphertext representative of the plaintext vector, and communicating the linearity ciphertext and the proximity ciphertext to an authentication server.

Abstract: Systems, methods, and instrumentalities are disclosed for joining a node to a network, the method comprising a station associated with a first node sending a first beacon, wherein the first beacon is sent with an indication that the first beacon is sent from a station entity, and wherein the station associated with the first node belongs to a first personal basic service set (PBSS); the station associated with the first node receiving a transmission from a station associated with a second node that indicates that the station associated with the second node wants to associate with the station associated with a first node, wherein the station associated with the second node is unassociated with the first PBSS; the station associated with the first node sending a message to a PBSS Control Point (PCP) associated with a third node, wherein the message is associated with handover preparation; the station associated with the first node receiving acceptance to change personality to a PCP and perform handover; and the

Abstract: A method includes receiving biometric data, the biometric data non-uniformly distributed and processing the biometric data to a level of randomness as a plaintext vector, the level of randomness associated with a security level. The method also includes encrypting the plaintext vector using a relational linearity encryption scheme to generate a linearity ciphertext representative of the plaintext vector, encrypting the plaintext vector using a relational proximity encryption scheme to generate a proximity ciphertext representative of the plaintext vector, and communicating the linearity ciphertext and the proximity ciphertext to an authentication server.

Abstract: A method of equality verification using relational encryption including receiving a relational key that includes a first relational key component and a registration ciphertext that includes an encryption of a first plaintext data set. The method includes storing the registration ciphertext without decrypting the registration ciphertext. After the storing of the registration ciphertext, the method includes receiving an authentication request and communicating a safeguard data set that includes a random challenge in response to the authentication request. The method includes receiving an encrypted response that is generated based on the safeguard data set and a second plaintext data set. The method includes verifying a relationship between the encrypted response and the registration ciphertext using the relational key without decrypting the encrypted response and without decrypting the registration ciphertext. The relationship indicates that equality exists between the first and the second plaintext data sets.

Abstract: A method of equality verification using relational encryption including receiving a relational key that includes a first relational key component and a registration ciphertext that includes an encryption of a first plaintext data set. The method includes storing the registration ciphertext without decrypting the registration ciphertext. After the storing of the registration ciphertext, the method includes receiving an authentication request and communicating a safeguard data set that includes a random challenge in response to the authentication request. The method includes receiving an encrypted response that is generated based on the safeguard data set and a second plaintext data set. The method includes verifying a relationship between the encrypted response and the registration ciphertext using the relational key without decrypting the encrypted response and without decrypting the registration ciphertext. The relationship indicates that equality exists between the first and the second plaintext data sets.

Abstract: A method includes receiving biometric data, the biometric data non-uniformly distributed and processing the biometric data to a level of randomness as a plaintext vector, the level of randomness associated with a security level. The method also includes encrypting the plaintext vector using a relational linearity encryption scheme to generate a linearity ciphertext representative of the plaintext vector, encrypting the plaintext vector using a relational proximity encryption scheme to generate a proximity ciphertext representative of the plaintext vector, and communicating the linearity ciphertext and the proximity ciphertext to an authentication server.

Abstract: A method includes receiving a first biometric data set representative of a first biometric sample provided by a user and public parameters. The method includes generating a first set of exchange information based thereon and communicating it to a system server. The method includes receiving a second set of exchange information based on the public parameters and a second biometric data set representative of a second biometric sample and is symmetric with respect to the first set of exchange information. The method includes computing a session key for the communication session by applying a first hash function based on a hash key to a subset of the second set of exchange information and a second hash function based on a projected key to a subset of the first set of exchange information. The method includes using the session key in communications during the communication session.

Abstract: A method includes receiving a first linearity ciphertext that represents a first biometric template encrypted using a relational linearity encryption scheme. The method includes receiving a second linearity ciphertext that represents a second biometric template encrypted using the relational linearity encryption scheme. The method includes discovering a linearity relationship between the first and second linearity ciphertexts using a linearity relational secret key. The method includes receiving a first proximity ciphertext that represents the first biometric template encrypted using a relational proximity encryption scheme. The method includes receiving a second proximity ciphertext that represents the second biometric template encrypted using the relational proximity encryption scheme.

Abstract: A method and apparatus for association in a mesh network may be disclosed. A method in a new node may include performing a discovery procedure with a plurality of peer nodes in the mesh network, initiating a temporary association procedure with each peer node, selecting a set of peer nodes from the plurality of peer nodes based on a selection algorithm at least based on a signal-to-interference and noise ratio (SINR) with each peer node and an interference impact of each peer node, and performing a final association with the selected set of peer nodes.

Abstract: A method includes transmitting initialization data including an error correction code and a matrix. The method also includes receiving registration data including a transformed registration biometric template, a registration user identifier, and a hashed registration code. The transformed registration biometric template may be determined based on a registration code word selected from the error correction code. The hashed registration code word may be determined by hashing the registration code word using the matrix. The registration user identifier may be associated with a first user described by the transformed registration biometric template.

Abstract: A method and apparatus are disclosed for communication in a Millimeter Wave Hotspot (mmH) backhaul system which uses mesh nodes. A mmH mesh node may receive a control signal which includes a total number of available control slots. The mesh node may determine the number of iterations of a resource scheduling mechanism that can be made during the time period of all available control slots, based on the number of neighbor nodes for the mesh node. Further, the mesh node may receive control slot information, including information about traffic queues and priorities. The mesh node may then perform resource scheduling using the resource scheduling mechanism based on the currently received control slot information and control slot information received in previous iterations of resource scheduling. The mesh node may also adjust a preamble based on a time between a last packet transmission and a current packet transmission to a neighboring node.

Abstract: Methods and apparatuses are described. A method of configuring a Radio Resource Control (RRC)_Connected wireless transmit/receive unit (WTRU) for wireless local area network (WLAN) cell measurement includes receiving, by the WTRU, an RRC ConnectionReconfiguration message. The RRC ConnectionReconfiguration message includes a measurement configuration that includes at least one WLAN measurement object on which the WTRU is to perform measurement and at least one measurement reporting configuration including at least an indication that measurement reporting is to be at least one of periodic and event-triggered. At least one measurement is performed on the at least one WLAN measurement object. A measurement report is provided based on the at least one measurement reporting configuration.

Abstract: A method includes extracting a set of enrollment feature points from an enrollment biometric measurement. The method also includes randomly selecting one or more enrollment code words from an error correction code. The method also includes determining obfuscated enrollment feature point data describing an obfuscated version of the set of feature points that is obfuscated using the one or more enrollment code words. The method also includes determining obfuscated enrollment code word data describing an obfuscated version of the one or more enrollment code words that is obfuscated using a random enrollment polynomial. The method also includes determining an enrollment biometric template including the obfuscated enrollment feature point data and the obfuscated enrollment code word data. The method also includes determining enrollment data including the enrollment biometric template. The enrollment data may be configured to keep the one or more enrollment code words and the random enrollment polynomial secret.