Abstract: A method for supporting sharing of travel history of travelers in airports, wherein the travelers' identity is managed using a distributed ledger system, wherein the distributed ledger system includes a global identity blockchain and several per segment security blockchains, wherein the global identity blockchain is accessible by entities of the distributed ledger system, and wherein a per segment security blockchain is employed for a predetermined flight segment, such that the per segment security blockchain is accessible only by entities of the distributed ledger system that are involved in the predetermined flight segment, the method comprising the steps of: a) generating a history secret of a traveler according to a resistance mechanism for resisting a creation of multiple identities, in particular a Sybil-resistance mechanism; b) receiving, by the global identity blockchain, a registration request of the traveler, wherein the registration request comprises a commitment of the traveler's history secret, a

Abstract: A method for securing a genuine machine learning model against adversarial samples includes the steps of attaching a trigger to a sample to be classified and classifying the sample with the trigger attached using a backdoored model that has been backdoored using the trigger. In a further step, it is determined whether an output of the backdoored model is the same as a backdoor class of the backdoored model, and/or an outlier detection method is applied to logits compared to honest logits that were computed using a genuine sample. These steps are repeated using different triggers and backdoored models respectively associated therewith. It is compared a number of times that an output of the backdoored models is not the same as the respective backdoor class, and/or a difference determined by applying the outlier detection method, against one or more thresholds so as to determine whether the sample is adversarial.

Abstract: A method for securing a genuine machine learning model against adversarial samples includes receiving a sample, as well as receiving a classification of the sample using the genuine machine learning model or classifying the sample using the genuine machine learning model. The sample is classified using a plurality of backdoored models, which are each a backdoored version of the genuine machine learning model. The classification of the sample using the genuine machine learning model is compared to each of the classifications of the sample using the backdoored models to determine a number of the backdoored models outputting a different class than the genuine machine learning model. The number of the backdoored models outputting a different class than the genuine machine learning model is compared against a predetermined threshold so as to determine whether the sample is an adversarial sample.

Abstract: A method for supporting identity management of travelers in an airport using a distributed ledger system includes receiving, by a global identity blockchain, a registration request from a traveler via a traveler device. The registration request includes a commitment for identity data that is uploaded by the traveler in a secure cloud storage. The method further includes recording the commitment in the global identity blockchain, receiving, by the global identity blockchain, a result of an identity verification with respect to the traveler from a verifier entity, recording the result in the global identity blockchain, and receiving, by a security blockchain, a ticket registration transaction issued by an airline entity. The ticket registration transaction comprises a unique traveler ID of the traveler. The method further includes issuing, by the security blockchain, an access control list update upon reception of consent by the traveler.

Abstract: A method prevents posterior-corruption long-range attacks in a proof of stake blockchain protocol in a blockchain network. The method includes: generating, by a blockchain node associated with a TEE device, a signing key pair, including a public key and a private key; remotely-attesting, by the blockchain node, a trusted enclave application, including generating an attestation certificate; and issuing, by the blockchain node, a registration transaction to distribute the attestation certificate; the registration transaction specifying an amount of mining stake purchased by the blockchain validator. Once the registration transaction is confirmed, the TEE device becomes enabled for mining blocks in the blockchain network.

Abstract: A method of generating a deniable commitment of personal data of a user with an unlinkable proof of the commitment of the personal data for securing user privacy in a digital identity system includes receiving the personal data of the user and receiving the commitment of the personal data according to a commitment scheme. An interactive zero-knowledge proof is engaged in with the user so as to verify that the commitment of the personal data opens to the personal data of the user.

Abstract: A method for securely sharing validation information of one or more data files stored on different cloud servers using distributed ledger technology includes requesting access to the data files and calculating a hash thereof. A structured Merkle tree is constructed using the hash and additional hashes of other data files for which a user has not granted access, but has used to construct a corresponding Merkle tree for which the user has committed a root value to a main blockchain. It is checked whether the root value of the Merkle tree is the same as the one the user has committed, and whether the hash of the data files is stored in a block of a satellite blockchain linked to the main blockchain and operated by a subset of nodes of the main blockchain that trust one another.

Abstract: The present invention relates to a method for registering a mining computing entity, ‘MCE’ with a trusted execution environment entity, ‘TEEE’ in a blockchain of a distributed blockchain consensus network, ‘DBCN’, based on a proof-of-stake protocol, said method comprising the steps of —Providing public signing and corresponding secret signing information and trusted time information by said TEEE of said MCE, —Providing public and secret account information for a virtual wallet of said blockchain by said MCE, —Generating integrity information by said TEEE, —Generating attestation information by signing said integrity information, hashed public signing information and public account information, —Computing proving information, by an attestation providing entity, ‘APE’, by attesting said attestation information, —Sending a transaction to said blockchain, signed with said secret account information, wherein said transaction including said public signing information and said proving information, —Verifying said tr

Abstract: A method prevents posterior-corruption long-range attacks in a proof of stake blockchain protocol on a blockchain network. The method includes: generating, by a blockchain node, a fresh key pair, having a fresh public key to be included into a transaction and a fresh private key to be used for signing a next transaction; generating, by the blockchain node, the transaction having as an input an overall stake associated to an account of the blockchain node, and as an output a transfer stake to be transferred to a second node's public key, and a remaining account stake to be transferred to the fresh public key; signing, by the blockchain node, the transaction with a previous private key; and broadcasting, by the blockchain node, the generated transaction to the blockchain network.

Abstract: A method for securely sharing validation information of one or more data files stored on different cloud servers using distributed ledger technology includes requesting access to the data files and calculating a hash thereof. A structured Merkle tree is constructed using the hash and additional hashes of other data files for which a user has not granted access, but has used to construct a corresponding Merkle tree for which the user has committed a root value to a main blockchain. It is checked whether the root value of the Merkle tree is the same as the one the user has committed, and whether the hash of the data files is stored in a block of a satellite blockchain linked to the main blockchain and operated by a subset of nodes of the main blockchain that trust one another.