International Business Machines Corporation

Padmanabhan Krishnan

Shrinivas Kulkarni

Vaibhav Murlidhar Kulkarni

Rachit Arora

Dharmesh Jain

Gopikrishnan Varadarajulu

Rakhi S Arora

Subin Shekhar

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 allocating a hyper-threaded processor to nodes of multi-tenant distributed software systems. Responsive to receiving a request to provision a node of the multi-tenant distributed software system on the host data processing system, a cluster to which the node belongs is identified. Responsive to the node being a second type of node, responsive to determining that another second type of node in the same cluster has been provisioned on the host data processing system, and responsive to the number of unallocated VPs on different physical processors from that of the other second type of node being greater than or equal to the requested number of VPs for the second type of node, the requested number of VPs for the second type of node is allocated each to a different physical processor from that of the other second type of node.

Hyper-threaded processor allocation to nodes in multi-tenant distributed software systems

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Bengaluru, India

Padmanabhan Krishnan