Secure Video Sharing via Sparse Vector Transformation
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Solution Overview
Problem
Existing data security methods fail to effectively protect video data while preserving relational properties, making it difficult to share securely in machine learning applications.
Innovation Solution
The method involves transforming video datasets into sparse vectors in a lower dimensional space using random projections, hiding original information while maintaining relational properties, and allowing secure sharing and reconstruction with knowledge of the transformation matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video data is transformed into sparse vectors using random projections, then data security is improved by hiding original information, but data utility may deteriorate due to dimensionality reduction
Solution Approach 1:
The patent transforms video data from its original high-dimensional space into a lower-dimensional sparse vector space using random projections. This dimensionality change achieves two goals: it obscures the original data structure for security purposes while preserving essential relational properties needed for machine learning tasks. The sparse vectors maintain sufficient information for model construction despite the reduced dimensionality.
Solution Approach 2:
The patent changes the parameter representation of video data by applying random projection transformations. The original video pixels are transformed into sparse vectors with different numerical characteristics, making the data unreadable in its original form while preserving the underlying patterns needed for learning. This parameter transformation enables secure data sharing without sacrificing analytical utility.
2Loss of information
If video data is shared in original form, then data utility is improved for machine learning, but data security deteriorates due to exposure of sensitive information
Solution Approach 1:
The patent introduces sparse vectors as an intermediary representation between the original video data and the machine learning model. These sparse vectors serve as a mediator that preserves the essential information needed for learning while obscuring the sensitive details of the original data. The transformation matrix acts as another intermediary that enables controlled access to the data's structural properties without exposing the raw information.
3Manufacturing precision
If video data is stored in original high-dimensional form, then data quality is improved, but storage requirements increase
Solution Approach 1:
The patent extracts only the essential features from the original video data by transforming it into sparse vectors. Instead of storing and processing all the original high-dimensional pixel information, the method extracts and retains only the critical relational properties needed for machine learning. This extraction process significantly reduces storage requirements while maintaining data quality sufficient for model construction.
Data Source
AI summary
In one example, the present disclosure describes a device, computer-readable medium, and method for enabling secure video sharing by exploiting data sparsity. In one example, the method includes applying a transformation to a video dataset containing a plurality of video samples, to produce a plurality of sparse vectors in a first dimensional space, wherein each sparse vector of the plurality of sparse vectors corresponds to one video sample of the plurality of video samples, and multiplying each sparse vector of the plurality of sparse vectors by a transformation matrix to produce a plurality of reduced vectors in a second dimensional space, wherein the dimension of the second dimensional space is smaller than a dimension of the first dimensional space, and wherein the plurality of reduced vectors in the second dimensional space hides information about the video dataset while preserving relational properties between the plurality of video samples.