International Business Machines Corporation

Rakhi S Arora

Shrinivas Kulkarni

Vaibhav Murlidhar Kulkarni

Rachit Arora

Dharmesh Jain

Gopikrishnan Varadarajulu

Anjali Agarwal

Padmanabhan Krishnan

Samir Katti

In a secure multi-party computation (sMPC) system, a super mask is constructed using a set of masks corresponding to a set of data contributors. Each data contributor uses a corresponding different mask to obfuscate the data of the data contributor. a first scaled masked data is formed by applying a first scale factor to first masked data of the first data contributor, the scale factor being computed specifically for the first data contributor from the super mask. A union is constructed of all scaled masked data from all data contributors, including the first scaled masked data. A machine learning (ML) model is trained using the union as training data, where the union continues to keep obfuscated the differently masked data from the different data contributors. The training produces a trained ML model usable in the sMPC with the set of data contributors.

Machine learning with differently masked data in secure multi-party computing

Textual masking for multiparty computation is provided. The method comprises receiving masked input data from a number of contributors, wherein the input data from each contributor has a unique contributor mask value. A unique analyst mask factor is received for each contributor, computed by an analyst as a difference between a uniform analyst mask value and the contributor mask value. An API call is received from the analyst to aggregate the input data from the contributors. The respective analyst mask factors are added to the input data from the contributors, and the data is aggregated and shuffled. Computational results received from the analyst based on the aggregated input data are published. In response to API calls from the contributors, the analyst mask factors are removed from the computational results, wherein computational results received by each contributor are masked only by the respective contributor mask value.

Masking text data for secure multiparty computation

A mechanism is provided for automatically recovering one or more distributed cluster nodes on a host system. Responsive to the host system recovering, restoring, or restarting, a set of distributed clustered systems affected due to the reboot of the host system and details associated with each of the set of distributed clustered systems are identified. Using the details, a set of nodes that operate on the host system are identified. For each node: respective components and configurations are prepared; a heartbeat is initiated thereby causing the node to register with a cluster network and identify peers within their associated distributed clustered system; a determination is made as to whether the node has reestablished connection with its associated distributed clustered system; and, responsive to the node reestablishing connection with its associated distributed clustered system, service components are started on the node.

Self-recoverable multitenant distributed clustered systems

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Bengaluru, India

Rakhi S Arora