Function Splitting for Private Collaborative Data Computing
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Solution Overview
Problem
Existing cloud computing services face challenges in facilitating data collaboration among entities while ensuring data privacy and preventing unauthorized data access, particularly when dealing with large datasets that are impractical to share due to privacy concerns and volume issues.
Innovation Solution
A function split method is employed, where a computation is divided into a first function performed by individual clients on their own datasets and a second function performed by a cloud service using distorted results, with a distortion function applied to protect data privacy and prevent the service from learning raw values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is shared among entities for collaborative computing, then data collaboration and analysis capabilities are improved, but data privacy and security are compromised
Solution Approach 1:
The patent segments the computation process into two distinct functions: a first function executed locally by each entity on their own dataset, and a second function executed by the cloud service on aggregated results. This segmentation allows collaborative computing while preventing raw data exposure, as each entity processes data locally and only shares computed results rather than raw datasets.
Solution Approach 2:
The patent introduces an intermediary computing model where the cloud service acts as a mediator that receives processed results from multiple entities rather than raw data. The cloud service performs the second function on these intermediate results, enabling collaboration without direct data sharing between entities or exposure to the service provider.
2Power
If large datasets are transferred to cloud service for processing, then computing power and analysis capabilities are improved, but network bandwidth and storage requirements increase
Solution Approach 1:
The patent extracts only the essential computed results from local processing and transfers them to the cloud service, rather than transferring entire large datasets. By taking out only the necessary intermediate results from the first function execution, the system minimizes data transfer volume while still enabling the cloud service to perform the second function for collaborative analysis.
Solution Approach 2:
The patent performs preliminary processing of data locally at each entity before cloud transmission. The first function is executed locally to pre-process datasets and generate condensed results, which are then transferred to the cloud. This preliminary action reduces the quantity of data that needs to be transferred and stored in the cloud, while still providing access to computing power for the second function.
Data Source
AI summary
Described are techniques for secure collaboration among clients of a computing service. The techniques include distributing, to a set of clients, a first function of a function split to be performed by individual clients included in the set of clients on individually owned datasets to generate individual results. The techniques further include distributing, to the set of clients, a distortion function to be performed by the individual clients on the individual results of the first function to generate distorted results. The techniques further include receiving, from the set of clients, the distorted results of the first function generated by the individual clients. The techniques further include performing a second function of the function split using the distorted results of the first function to produce a return result.