Multi-Level Data Signature Detection for Cloud Leakage
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Solution Overview
Problem
Data leakage detection in cloud storage systems is challenging due to the difficulty in distinguishing between original and copied datasets, particularly in cases of stolen trade secrets, copyrighted data, and classified information, where existing methods fail to effectively identify similarities and prevent misuse.
Innovation Solution
A data detection system that generates multi-level evaluation data abstraction representations, or signatures, for datasets, allowing for comparison and determining similarity scores, which triggers actions when a predefined threshold is exceeded, indicating potential data leakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data storage methods are used in cloud platforms, then data storage capacity and accessibility are improved, but the ability to detect data leakage and stolen data is insufficient
Solution Approach 1:
The system performs preliminary actions by generating cryptographic hashes and multi-level signatures of datasets immediately when they are uploaded to the cloud platform, before any potential leakage can occur. These signatures are stored in a database for future comparison, enabling proactive detection rather than reactive response to data theft
Solution Approach 2:
The patent introduces cryptographic hashes and multi-level signatures as intermediary representations of the actual data. Instead of directly comparing large datasets (which would be computationally expensive), the system compares these condensed signature representations, making the detection process efficient and scalable while maintaining high accuracy
2Measurement precision
If multi-level signature comparison is performed for every uploaded dataset, then data leakage detection accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the data comparison process into multiple hierarchical levels. Instead of performing a single comprehensive comparison, the system divides the signature comparison into levels (e.g., full hash comparison, then selective segment comparison), allowing it to quickly eliminate non-matching datasets at higher levels and only perform detailed comparisons when necessary, thus reducing overall computational complexity
Solution Approach 2:
The system performs partial comparison actions by first comparing condensed signature representations rather than the complete datasets. This partial action (comparing signatures instead of full data) provides sufficient detection accuracy for most cases, and only triggers full dataset comparison when the signature similarity exceeds a threshold, avoiding excessive computational effort
Data Source
AI summary
A computer-implemented system and method for a data leakage and misuse detection system comprises receiving an evaluation dataset A, and building a signature of the evaluation dataset A (sig(A)), where A signature of a dataset is a multi-level evaluation data abstraction representation of the dataset. The method further comprises building a signature for each of existing datasets B (B1, B2, . . . , Bn) (sig(Bx)) that are stored in a memory. The method then compares the sig(A) with each of the sig(Bx)s. A similarity score is derived based on the comparing, and responsive to determining the similarity score exceeds a predefined threshold, the method comprises generating an activity related to the determination.