Privacy-Preserving Cloud Data Processing via Secret Sharing
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Solution Overview
Problem
Current systems for leveraging the wisdom of the crowd in data processing, such as crowdsensing and crowdsourcing, face challenges in ensuring data privacy and quality management, particularly in real-time aggregation and monetization of knowledge, where existing methods lack efficient privacy protection and fair reward mechanisms.
Innovation Solution
A privacy-aware crowdsensing framework that uses encrypted data streams processed by multiple cloud servers with secret sharing and blockchain-based technology for secure truth discovery and knowledge monetization, ensuring data privacy and fairness through exponential weighted moving averages and quality-aware rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If encrypted data streams are processed by multiple cloud servers using secret sharing, then data privacy is improved, but processing complexity increases
Solution Approach 1:
The patent divides the encrypted data into multiple shares and distributes them across different cloud servers. Each server processes only its portion of the encrypted data, unable to access the complete information. This segmentation enables privacy-preserving processing while distributing the computational complexity across multiple independent units rather than concentrating it in a single system.
2Productivity
If real-time aggregation of crowd data is implemented, then productivity is improved, but data quality management becomes more difficult
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors and evaluates the quality of incoming crowd data in real-time. Based on this feedback, the system dynamically adjusts data processing parameters, filters low-quality inputs, and prioritizes high-quality data streams. This enables efficient real-time aggregation while maintaining stringent data quality control through continuous monitoring and adaptive adjustment.
3Loss of information
If blockchain technology is used for knowledge monetization, then transparency is improved, but system complexity increases
Solution Approach 1:
The patent introduces blockchain technology as an intermediary layer that mediates between the data processing system and the monetization mechanism. The blockchain serves as a transparent, decentralized ledger that records all transactions and data contributions immutably. This intermediary enables transparent knowledge monetization by providing verifiable proof of data contribution and automatic reward distribution, while the modular integration allows the complex blockchain functionality to be added as a separate layer rather than embedded throughout the entire system.
Data Source
AI summary
A method including receiving, at multiple cloud computing servers, multiple streaming data sets for the same sensing task each from a respective client device. The streaming data set from each client device comprises sensed data sensed by one or more sensors of said client device. The streaming data sets are encrypted. Each respective streaming data set from a respective client device is divided into multiple streaming data set portions, each to be received at a respective one of the cloud computing server. The method also includes processing, at each respective cloud computing server, the corresponding streaming data set portions received to generate a corresponding share of a result for the sensing task. The method also includes encrypting, at each respective one of the cloud computing servers, the corresponding share of the result; and facilitating creation or update of a blockchain based on the encrypted shares of the result.