Secure Edge Storage Network Resilient to Byzantine Attacks
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Solution Overview
Problem
Distributed data storage systems face challenges in providing real-time data analysis and storage for time-critical applications like smart cities and autonomous cars, due to high latency and network unpredictability in cloud storage, especially when dealing with exponentially growing data volumes and heterogeneous edge devices with limited capabilities.
Innovation Solution
A secure distributed heterogeneous edge storage system is implemented, which partitions files, generates keys, and stores masked packets across edge nodes, incorporating error correction codes to ensure resilience against eavesdropping and Byzantine attacks, optimizing storage node selection and memory allocation to minimize costs and ensure data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in centralized cloud data centers, then data storage capacity is sufficient, but latency is high and real-time data analysis is difficult
Solution Approach 1:
The patent divides the centralized cloud storage system into distributed edge storage nodes. Data is partitioned and stored across multiple edge devices (smartphones, tablets, wearables, IoT devices) instead of a single centralized data center. This segmentation brings storage closer to data sources and consumers, reducing latency while maintaining total storage capacity through the collective resources of numerous distributed nodes.
2Loss of time
If data is distributed across multiple edge nodes, then latency is reduced and real-time analysis is enabled, but security against eavesdropping and Byzantine attacks becomes more challenging
Solution Approach 1:
The patent applies secret sharing schemes and error correction codes before distributing data to edge nodes. The file is encoded into multiple shares with embedded redundancy and security mechanisms in advance. This preliminary encoding ensures that even if some nodes are compromised or return corrupted data, the original file can be securely reconstructed without exposing sensitive information to eavesdroppers or Byzantine attackers.
Solution Approach 2:
The patent introduces secret sharing schemes and error correction codes as intermediary layers between the original data and the distributed storage nodes. These intermediaries transform the data into a form that provides built-in protection against eavesdropping and Byzantine attacks, allowing secure reconstruction without direct access to individual node contents.
3Adaptability or versatility
If heterogeneous edge devices are used for storage, then device capabilities are utilized efficiently, but coordination and management complexity increases
Solution Approach 1:
The patent implements a universal secret sharing framework that works across diverse heterogeneous edge devices. The encoding and decoding mechanisms are designed to be device-agnostic, allowing smartphones, tablets, wearables, and IoT devices with different capabilities to participate in the same distributed storage system. This universality simplifies coordination by providing a common interface and protocol that abstracts away device-specific variations.
Data Source
AI summary
Distributed storage of a file in edge storage devices that is resilient to eavesdropping adversaries and Byzantine adversaries. Approaches include a cost-efficient approach in which an authorized user has access to the content of all edge storage nodes. In this approach, key blocks and file blocks that are masked with key blocks are saved in the edge storage nodes. Additionally, redundant data for purposes of error correction are also stored. In turn, upon retrieval of all blocks, errors introduced by a Byzantine adversary may be corrected. In a loss resilient approach, redundant data is stored along with masked file partitions. Upon retrieval of blocks from the edge storage nodes, a unique approach to solving for the unknown file partition values is applied with identification of corrupt nodes based on an average residual error value for each storage node.