Homomorphic Encryption Memory Optimization
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Solution Overview
Problem
Standard encryption methods prevent encrypted data from being analyzed or processed without decryption, making it difficult for untrusted devices to securely process data, and homomorphic encryption increases memory and bandwidth requirements significantly.
Innovation Solution
The system employs a client compute device that packs multiple parameters into a single ciphertext block and sends it to a cloud compute device for remote processing, using a shapeshifter to create operands on the fly from ciphertext blocks, reducing memory and bandwidth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If homomorphic encryption is used to process encrypted data without decryption, then data security is improved, but memory and bandwidth requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by packing multiple parameters into ciphertext blocks before processing, and by pre-organizing data in memory buffers. This preparation reduces the overall memory footprint and bandwidth requirements during the actual homomorphic encryption operations, as data is efficiently structured in advance to minimize redundant storage and transmission.
Solution Approach 2:
The patent implements nesting by packing multiple parameters into single ciphertext blocks, and by organizing data structures where smaller data elements are nested within larger containers. This nested organization allows the system to process multiple pieces of information simultaneously using homomorphic encryption, reducing the total volume of memory and bandwidth resources required compared to processing each parameter separately.
2Quantity of substance
If multiple parameters are packed into a single ciphertext block for remote processing, then bandwidth requirements are reduced, but processing complexity increases
Solution Approach 1:
The system segments the processing task by separating data preparation operations (performed locally with lower complexity) from homomorphic encryption operations (performed remotely). The local device handles parameter packing and initial processing, while the remote device focuses on secure encrypted computations. This segmentation reduces bandwidth requirements by minimizing data transmission while managing processing complexity across different system components.
Solution Approach 2:
The patent introduces an intermediary layer of encrypted data structures and buffer management that mediates between the local processing environment and remote homomorphic encryption operations. This intermediary mechanism handles the complexity of packed parameter management, memory buffer coordination, and encrypted data flow, reducing the apparent complexity at both endpoints while maintaining efficient bandwidth utilization.
3Quantity of substance
If operands are created on the fly from ciphertext blocks, then memory requirements are reduced, but execution time increases due to additional processing steps
Solution Approach 1:
The system employs periodic action by creating operands in batches from ciphertext blocks rather than individually, and by using buffer-based processing that operates in periodic cycles. This batched approach reduces memory requirements by reusing buffer structures while minimizing execution time overhead through efficient bulk processing operations, avoiding the need to allocate and manage separate memory structures for each operand.
Solution Approach 2:
The patent implements self-service mechanisms where the homomorphic encryption processor automatically manages operand creation from ciphertext blocks using predefined buffer structures and algorithms. The system includes self-managing memory buffers that automatically allocate, initialize, and manage their own structures, reducing the need for external memory management overhead and minimizing execution time while maintaining reduced memory footprint.
Data Source
AI summary
Technologies for memory and I/O efficient operations on homomorphically encrypted data are disclosed. In the illustrative embodiment, a cloud compute device is to perform operations on homomorphically encrypted data. In order to reduce memory storage space and network and I/O bandwidth, ciphertext blocks can be manipulated as data structures, allowing operands for operations on a compute engine to be created on the fly as the compute engine is performing other operations, using orders of magnitude less storage space and bandwidth.