One-Time Pad Scrambling for Private Cloud Document Classification
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Solution Overview
Problem
Organizations face challenges in utilizing cloud-based machine learning services for document classification due to privacy and secrecy concerns, as existing encryption methods like asymmetric encryption, homomorphic encryption, and differential privacy techniques fail to maintain strict data confidentiality and computational efficiency.
Innovation Solution
A system using an encoder and deconvolution unit with a neural network to scramble documents with a one-time pad configuration, combined with an internal inference network to decode cloud classifications, ensuring confidentiality and high classification quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If asymmetric encryption is used to send data to the cloud, then data can be transmitted securely, but the cloud provider gains unfettered access to the organization's private data
Solution Approach 1:
The system performs preliminary scrambling of the data using a one-time pad key before transmission to the cloud. This preliminary action ensures that the cloud provider receives only scrambled data that cannot be interpreted without the key, eliminating the harmful effect of cloud provider access while maintaining transmission security.
Solution Approach 2:
The one-time pad key acts as an intermediary that transforms the original data into scrambled form. This intermediary mechanism allows secure transmission without exposing the underlying private data to the cloud provider, resolving the contradiction between transmission security and data confidentiality.
2Object-affected harmful factors
If homomorphic encryption is used to process data in encrypted form, then data privacy is maintained, but processing speed becomes extremely slow and requires significantly higher processing capabilities
Solution Approach 1:
The system extracts the scrambling operation from the processing pipeline and performs it separately using simple XOR operations with the one-time pad key. This extraction allows the main processing to occur on scrambled data without the computational overhead of homomorphic encryption, while still maintaining privacy by keeping the key secret.
Solution Approach 2:
The system uses a disposable one-time pad key that is used only once for scrambling and then discarded. This approach replaces the computationally expensive homomorphic encryption with a simple, inexpensive scrambling operation that achieves the same privacy goal without the performance penalty.
3Object-affected harmful factors
If organizations use in-house machine learning resources to maintain data privacy, then data confidentiality is preserved, but the accuracy and capability of classification systems are limited
Solution Approach 1:
The system performs preliminary scrambling of data using a one-time pad before sending it to the cloud for processing. This preliminary action enables the use of high-accuracy cloud-based classification systems while maintaining data confidentiality, as the scrambled data cannot be interpreted without the key.
Solution Approach 2:
The one-time pad key serves as an intermediary that enables secure communication with cloud-based classification systems. This intermediary mechanism allows organizations to leverage the high accuracy of cloud systems without sacrificing data confidentiality, resolving the contradiction between using external resources and maintaining privacy.
4Object-affected harmful factors
If a one-time pad configuration is used to scramble documents, then document secrecy is maintained, but the system complexity increases
Solution Approach 1:
The system replaces complex cryptographic mechanisms with simple XOR-based scrambling operations using a one-time pad. This substitution maintains document secrecy while reducing system complexity, as the scrambling operation is computationally trivial compared to traditional encryption methods.
Solution Approach 2:
The system changes the parameter of the scrambling operation from complex cryptographic transformations to simple XOR operations with a one-time pad key. This parameter change maintains the security property of document secrecy while dramatically simplifying the system structure and operations.
Data Source
AI summary
An organization's system is configured to label a given document based on an on-cloud classification service, while maintaining confidentiality of the document's content from all entities external to the organization, including: (a) an encoder configured to receive the given document, and to create an embedding of the given document; (b) a deconvolution unit having a neural network, wherein weights of neurons within the neural network are defined relative to a key, the deconvolution unit is configured to receive the embedding, deconvolve the embedding, thereby to create a scrambled document which is then sent to the on-cloud classification service; (c) a pre-trained internal inference network, configured to: (i) receive from the on-cloud service a cloud-classification of the scrambled document, (ii) to also receive a copy of the embedding, and (ii) to identify, given the received cloud-classification and the embedding copy, a true label of the given document.