The proposed synthesis framework allows users to share and process data in the cloud while ensuring privacy through coding mechanisms that maintain data utility and algorithmic performance.
Our framework addresses privacy concerns in cloud computing by enabling users to run algorithms on distorted data without disclosing original data, thereby ensuring substantial protection.
Key components of the proposed solution include the design of coding mechanisms that ensure differential privacy and a decoding function that retrieves true utility despite distortions.
This innovative approach allows for practical cloud computing applications, balancing the need for privacy against the importance of maintaining algorithmic performance and data utility.
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