Decoy Data Sharding With Quantum Processing for Web Security
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Solution Overview
Problem
Web-based environments face threats and cyberattacks, necessitating improved methods for generating decoy data and sharding sensitive data to enhance security and efficiency.
Innovation Solution
A combined classical and quantum computing system generates decoy data and shards sensitive data using machine-learning models to isolate adversarial interactions, utilizing quantum computing for parallel processing to improve efficiency and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum computing is used for parallel processing to generate decoy data, then productivity and speed are improved, but device complexity increases
Solution Approach 1:
The system segments the computing workload by separating quantum processing (for parallel decoy data generation) from classical processing (for adversarial interaction detection and coordination). This allows the quantum processor to handle only the computationally intensive parallel generation task while the classical system manages detection and system coordination, thereby improving productivity without requiring the entire system to be quantum-based.
Solution Approach 2:
A classical computing system acts as an intermediary between the quantum processor and the adversarial interaction detection mechanisms. The classical system receives adversarial interaction detections, coordinates with the quantum processor to generate appropriate decoy data sequences, and manages the overall system workflow. This intermediary approach enables the integration of quantum parallel processing capabilities while maintaining system manageability and reducing overall complexity.
2Adaptability or versatility
If machine-learning models are used to generate decoy data in real-time, then adaptability is improved, but use of energy increases
Solution Approach 1:
The machine-learning models are trained in advance on classical computing systems to learn the patterns and characteristics of sensitive data. This preliminary training phase allows the models to be deployed in a ready state, reducing the computational energy required during real-time decoy data generation. The quantum processor then leverages these pre-trained models for efficient parallel generation of adaptive decoy data sequences.
Solution Approach 2:
Instead of performing complex real-time analysis on actual sensitive data, the system creates copies or representations of data patterns through the trained machine-learning models. These model-based copies enable the generation of realistic-looking decoy data without requiring continuous access to or processing of the original sensitive data, thereby reducing energy consumption while maintaining adaptability.
Data Source
AI summary
A system includes a memory configured to store instances of a software application executable on a computing device and a set of sensitive data and a processor operably coupled to the memory and configured to detect an interaction to initiate an execution of user interactions with the set of sensitive data. The processor is further configured to execute one or more generative machine-learning models trained to generatively present sequences of different decoy data to a user in response to an execution of interactions with the different decoy data, partition the set of sensitive data into sets of sharded sensitive data, and transfer the sets of sharded sensitive data to a trusted database and onto a trusted computing node. The processor is further configured to store a log of the generatively presented sequences of different decoy data and the execution of the interactions with the different decoy data.