Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Database tables can have different types of database indices defined for the database tables and different numbers of database indices. The efficiency of reading the indexes can vary with the different profiles of the indexes, which impacts the costs of access plans that use the indexes. Weights can be predefined to reflect the relative efficiencies of the different characteristics. Costs can be computed in accordance with a variety techniques (e.g., based on edge traversals). The weights can be predefined to reduce costs, increase costs, or a combination thereof. A database management application or associated application or program can also refine or revise these weights based on statistical data gathered about the operation of the database and/or heuristics that are developed based on observations/research. The corresponding weights can be adjusted accordingly.

Abstract: Database tables can have different types of database indices defined for the database tables and different numbers of database indices. The efficiency of reading the indexes can vary with the different profiles of the indexes, which impacts the costs of access plans that use the indexes. Weights can be predefined to reflect the relative efficiencies of the different characteristics. Costs can be computed in accordance with a variety techniques (e.g., based on edge traversals). The weights can be predefined to reduce costs, increase costs, or a combination thereof. A database management application or associated application or program can also refine or revise these weights based on statistical data gathered about the operation of the database and/or heuristics that are developed based on observations/research. The corresponding weights can be adjusted accordingly.

Abstract: Processor performance in executing a section of software code is measured. A clock associated with the section of software code. It is detected that the section of software code is beginning to be executed. In response, the clock associated with the section of software code is started. It is then detected that the section of software code is no longer being executed. In response, the clock associated with the section of software code is stopped. The clock is stopped and restarted as the section of software code temporarily stops execution to cede control to other sections of software code. Ultimately, a value of the clock associated with the section of software code is output, where the value corresponds to the processor performance in executing the section of software code.

Abstract: There is disclosed a system and method for optimising database access plans. Specifically, modifications made to a database are monitored and compared to a set of criteria. If a modification satisfies a criterion, the access plans relating to the modification are updated.

Abstract: Database tables can have different types of database indices defined for the database tables and different numbers of database indices. The efficiency of reading the indexes can vary with the different profiles of the indexes, which impacts the costs of access plans that use the indexes. Weights can be predefined to reflect the relative efficiencies of the different characteristics. Costs can be computed in accordance with a variety of techniques (e.g., based on edge traversals). The weights can be predefined to reduce costs, increase costs, or a combination thereof. A database management application or associated application or program can also refine or revise these weights based on statistical data gathered about the operation of the database and/or heuristics that are developed based on observations/research. The corresponding weights can be adjusted accordingly.

Abstract: There is disclosed a system and method for optimising database access plans. Specifically, modifications made to a database are monitored and compared to a set of criteria. If a modification satisfies a criterion, the access plans relating to the modification are updated.

Abstract: Processor performance in executing a section of software code is measured. A clock associated with the section of software code. It is detected that the section of software code is beginning to be executed. In response, the clock associated with the section of software code is started. It is then detected that the section of software code is no longer being executed. In response, the clock associated with the section of software code is stopped. The clock is stopped and restarted as the section of software code temporarily stops execution to cede control to other sections of software code. Ultimately, a value of the clock associated with the section of software code is output, where the value corresponds to the processor performance in executing the section of software code.