Nec Corporation

Giorgia Azzurra Marson

Ghassan Karame

Sebastien Andreina

Wenting Li

Helen Moellering

Jens-Matthias Bohli

Claudio Soriente

Kumar Sharad

Lorenzo Alluminio

Pascal Zimmer

Elina Van Kempen

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, includes determining public signing information, secret signing information, and a registration timestamp and determining public account information and secret account information for a virtual wallet of the blockchain. The method further includes generating attestation information based on signing integrity information and hashing the public signing information and the public account information, and based on the attestation information, obtaining, from an attestation providing entity (APE), proving information. The method also includes sending, to the blockchain, a registration transaction that is signed with the secret account information, and registering the MCE to the blockchain.

Method for signing a new block in a decentralized blockchain consensus network

A method for secure chain division of a satellite chain by a validator node of a permission-based blockchain system includes executing, by communicating with a set of validator nodes of an original satellite chain of the blockchain system, a validator assignment scheme that splits the set of validator nodes of the original satellite chain into subsets of validator nodes of child chains of the original satellite chain, and running, by communicating with the validator nodes of the respective subsets, a reconfiguration protocol to set up the respective child chains and sending, to an identity management component that maintains identity information of all members of the blockchain system in a registry, a configuration update to record the division of the original satellite chain and corresponding creation of the child chains.

Method and system for scaling blockchains via secure chain division

A method for performing a privacy-preserving membership test includes performing an oblivious pseudo-random function (OPRF) protocol to determine a pseudo-random function (PRF) result based on an input from a proving device and a PRF key. The input indicates a user identity of a user associated with the providing device. The method further includes determining whether the user belongs to a verifier list associated with a verifier device based on testing membership of the user using the verifier list and the PRF result.

Augmented zero-knowledge proof with private membership test

A method for thwarting attacks on a machine-learning (ML) model is provided. The method includes determining, by the ML model, a classification vector based on an input. The method further includes evaluation the classification vector based on a threshold parameter to determine a threshold result. The method also includes outputting a classification prediction based on the threshold result.

Method and system for thwarting attacks on machine-learning as a service

A method for secure aggregation using public randomness is provided. The method includes determining, based on the public randomness from a random service beacon, a set of super-clients from a plurality of clients. The method further includes obtaining masked inputs from the clients and aggregated random values based on the set of super-clients. Each of the masked inputs is associated with a client from the plurality of clients and each of the aggregated random values is associated with a super-client from the set of super-clients. The method also includes aggregating the masked inputs from the plurality of clients and the aggregated random values from the set of super-clients.

Single-server secure aggregation with public randomness

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.

Securing machine learning models against adversarial samples through backdoor misclassification

A method for performing federated learning includes initializing, by a server, a global model G. The server shares Gwith a plurality of participants (N) using a secure communications channel. The server selects n out of N participants, according to filtering criteria, to contribute training for a round r. The server partitions the selected participants n into s groups and informs each participant about the other participants belonging to the same group. The server obtains aggregated group updates AU, . . . , AUfrom each group and compares the aggregated group updates and identifies suspicious aggregated group updates. The server combines the aggregated group updates by excluding the updates identified as suspicious, to obtain an aggregated update U. The server derives a new global model Gfrom the previous model Gand the aggregated update Uand shares Gwith the plurality of participants.

System for secure federated learning

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, includes providing public signing and corresponding secret signing information and trusted time information by the TEEE of the MCE, providing public and secret account information for a virtual wallet of the blockchain by the MCE, and generating integrity information by the TEEE. The method further includes generating attestation information by signing the integrity information, hashed public signing information and public account information, computing proving information, by an attestation providing entity (APE), by attesting the attestation information, and sending a transaction to the blockchain, signed with the secret account information, the transaction including the public signing information and the proving information.

A method detects model-poisoning attempts in a federated learning system. The federated learning system includes a server orchestrating with clients to train a machine-learning model. The method includes receiving, by the server, results of a poisoning detection analysis. The poisoning detection analysis includes at least one of an analysis of class-specific misclassification rates or an analysis of activation clustering of a current state of the machine-learning model.

Thwarting model poisoning in federated learning

A computer-implemented method of instantiating a machine learning model with a host processing system is provided. The host processing system includes a trusted execution environment (TEE) and an untrusted processing system (UPS). The method includes: preparing, with the host processing system, a compiler encoding an architecture of the machine learning model; receiving, from a client processing system, source data; and producing, with the compiler, software based on the received source data and model parameters stored on the host processing system. The software includes an untrusted software component for performance on the UPS and a trusted software component for performance on the TEE. The untrusted software component and the trusted software component are configured to, when performed in concert, instantiate the machine learning model.

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Heidelberg, Germany

Giorgia Azzurra Marson