International Business Machines Corporation

Runhua Xu

Nathalie Baracaldo Angel

Heiko Hary Ludwig

Yi Zhou

Omri Soceanu

Hayim Shaul

Ali Anwar

Ehud Aharoni

Eyal Kushnir

An example system includes a processor to compute a tensor of indicators indicating a presence of partial sums in an encrypted vector of indicators. The processor can also securely reorder an encrypted array based on the computed tensor of indicators to generate a reordered encrypted array.

Secure reordering using tensor of indicators

A second set of data identifiers, comprising identifiers of data usable in federated model training by a second data owner, is received at a first data owner from the second data owner. An intersection set of data identifiers is determined at the first data owner. At the first data owner according to the intersection set of data identifiers, the data usable in federated model training is rearranged by the first data owner to result in a first training dataset. At the first data owner using the intersection set of data identifiers, the first training dataset, and a previous iteration of an aggregated set of model weights, a first partial set of model weights is computed. An updated aggregated set of model weights, comprising the first partial set of model weights and a second partial set of model weights from the second data owner, is received from an aggregator.

Private vertical federated learning

A plurality of public encryption keys are distributed to a plurality of participants in a federated learning system, and a first plurality of responses is received from the plurality of participants, where each respective response of the first plurality of responses was generated based on training data local to a respective participant of the plurality of participants and is encrypted using a respective public encryption key of the plurality of public encryption keys. A first aggregation vector is generated based on the first plurality of responses, and a first private encryption key is retrieved using the first aggregation vector. An aggregated model is then generated based on the first private encryption key and the first plurality of responses.

Privacy-preserving federated learning

Systems and techniques that facilitate universal and efficient privacy-preserving vertical federated learning are provided. In various embodiments, a key distribution component can distribute respective feature-dimension public keys and respective sample-dimension public keys to respective participants in a vertical federated learning framework governed by a coordinator, wherein the respective participants can send to the coordinator respective local model updates encrypted by the respective feature-dimension public keys and respective local datasets encrypted by the respective sample-dimension public keys. In various embodiments, an inference prevention component can verify a participant-related weight vector generated by the coordinator, based on which the key distribution component can distribute to the coordinator a functional feature-dimension secret key that can aggregate the encrypted respective local model updates into a sample-related weight vector. In various embodiments, the inference prevention component can verify the sample-related weight vector, based on which the key distribution component can distribute to the coordinator a functional sample-dimension secret key that can aggregate the encrypted respective local datasets into an update value for a global model.

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Pittsburgh, PA, United States of America

Runhua Xu