Telecom Italia S.p.a.

Madalina Baltatu

Roberta D'Amico

Jovan Golic

Rosalia D'Alessandro

Dario Lombardo

Luciana Costa

Paolo Abeni

Sebastiano Di Paola

Massimo Tistarelli

Enrico Grosso

Manuele Bicego

A method of protecting the exchange of privacy-sensitive data in a wireless communication network, the method including generating and providing a public cryptographic key to a first entity, possessing the data to be sent to the network through a wireless connection; generating and providing a private secret cryptographic key to a second entity, being the intended recipient of the data, the private key being bound to the public key and being associated to privacy support context information to identify the network; having the network send to the first entity respective wireless network privacy support context information; having the first entity receive the information and encrypt the data using the public key and the received information to obtain protected, encrypted privacy-sensitive data; having the first entity send to the second entity through the network the encrypted data; and having the second entity decrypt the encrypted data exploiting the private key.

Protection of privacy in wireless telecommunication networks

Disclosed herein is an anomaly detection method for a packet-based network which includes several network resources, also called network-related software objects. The method includes monitoring the network resources of the packet-based network, ordering the monitored network resources according to a given ordering criterion, and detecting an anomaly in the packet-based network based on the ordered network resources. In particular, detecting an anomaly includes forming a detection feature vector based on the ordered network resources, and feeding the detection feature vector to a machine learning system configured to detect an anomaly in the packet-based network based on the detection feature vector. The detection feature vector includes detection feature items related to corresponding monitored network resources, and arranged in the detection feature vector depending on the ordering of the corresponding monitored network resources. Conveniently, the machine learning system is a one-class classifier, preferably a one-class Support Vector Machine (OC-SVM).

Anomaly detection for packet-based networks

Disclosed herein is an anomaly detection method for link-state routing protocols, a link-state routing protocol providing for link-state update (LSU) messages to be exchanged between nodes in a packet-based network, wherein each link-state update message includes link-state advertisement (LSA) message(s) each having a respective header. The method comprises monitoring the link-state advertisement messages exchanged in the network, extracting and forming respective feature vectors with the values in the fields of the headers of the monitored link-state advertisement messages, and detecting an anomaly related to routing based on the feature vectors. In particular, detecting an anomaly related to routing includes feeding the feature vectors to a machine learning system, conveniently a one-class classifier, preferably a one-class support vector machine (OC-SVM).

Anomaly detection for link-state routing protocols

A biometric user authentication method, includes enrolling a user based on user's biometric samples to generate user's reference data; and authenticating the user based on a user's live biometric sample and the user's reference data; wherein enrolling a user includes acquiring the user's biometric samples; extracting an enrollment feature vector from each user's biometric sample; computing a biometric reference template vector as a mean vector based on the enrollment feature vectors; computing a variation vector based on the enrollment feature vectors and the mean vector; randomly generating an enrollment secret vector; computing an enrollment code vector based on the enrollment secret vector and the variation vector; computing a difference vector as a wrap-around difference between the enrollment code vector and the mean vector; computing an error correction vector based on the enrollment secret vector to enable error correction during the user authentication phase according to a given error tolerance level, wherein the error correction vector is not computed if the error tolerance level is equal to zero; and storing the variation vector, the difference vector, and the error correction vector as a part of the user's reference data to be used during the user authentication phase.

Method and system for biometric authentication and encryption

An automatic biometric identification method based on face recognition and support vector machines, includes enrolling a user to generate a user's reference template; and identifying the user based on the user's reference template, wherein generating a user's reference template includes acquiring a number of user's face images, and training a one-class support vector machine based on the user's face images only.

Automatic biometric identification based on face recognition and support vector machines

A user authentication method based on the use of identification biometric techniques, including the steps of generating a reference biometric template from a first biometric image of a user to be authenticated; splitting the reference biometric template into a first and a second reference biometric template portion that can be physically separated; signing and enciphering the first and the second reference biometric template portion; storing the signed and enciphered first and second reference biometric template portion into different memories.

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Turin, Italy

Madalina Baltatu