Cloud Storage Bulk Operation Detection Model
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Solution Overview
Problem
Cloud storage synchronization can lead to accidental or nefarious data deletion and encryption, making it difficult for users to recover previous versions of their content, especially when bulk operations are performed without proper detection and approval mechanisms.
Innovation Solution
A system that detects bulk operations by creating a usage pattern model to define change thresholds, compares attempted operations to these models, and modifies preservation policies to increase versioning frequency and retention time, while notifying users for approval or rejection, and providing restoration options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic synchronization is enabled in cloud storage, then content is kept up-to-date across clients, but users risk accidental or malicious data deletion without immediate awareness
Solution Approach 1:
The system creates a usage pattern model that defines change thresholds before bulk operations occur. This preliminary model establishes baseline expectations for normal user behavior, enabling the system to detect and prevent abnormal bulk operations before they cause data loss. The model is created based on historical usage patterns and is ready to evaluate incoming operations.
Solution Approach 2:
The system continuously monitors content operations and compares them against the usage pattern model. When a bulk operation is detected that deviates from the model (exceeds change thresholds), the system provides feedback by notifying the user and seeking approval before execution. This closed-loop feedback mechanism ensures data safety while maintaining synchronization functionality.
2Ease of operation
If bulk operations are executed without detection, then user actions are not interrupted, but restoration of previous content versions becomes complex and tedious
Solution Approach 1:
The system modifies preservation policies in advance based on the usage pattern model before bulk operations occur. When the model indicates a bulk operation is likely, the system proactively increases versioning frequency and extends retention time for content versions. This preliminary action ensures that restoration points are already in place and easily accessible, making recovery simple even if the bulk operation proceeds.
Solution Approach 2:
The system automatically adjusts preservation policies and creates restoration points without requiring user intervention. The usage pattern model autonomously determines when to increase versioning frequency and extend retention, and the system self-manages the preservation policy modifications. This self-service approach simplifies restoration by making it an automatic, background process rather than a complex manual task.
3Reliability
If usage pattern modeling and bulk operation detection are implemented, then data protection is improved, but processor and memory usage increase
Solution Approach 1:
The system applies different levels of monitoring and protection to different content based on the usage pattern model. Instead of uniformly monitoring all content operations, the system focuses computational resources on content and operations that deviate from normal patterns. The change thresholds in the model enable selective attention to suspicious bulk operations while ignoring normal user activity, thereby reducing overall processor and memory usage.
Solution Approach 2:
The system dynamically adjusts preservation policy parameters (versioning frequency, retention time) based on the usage pattern model and detected operation characteristics. When bulk operations are detected, the system temporarily increases versioning frequency and extends retention time only for affected content. This parameter adaptation ensures strong protection when needed while minimizing resource consumption during normal operations.
Data Source
AI summary
Cloud-based storage services are provided for storing and/or sharing content across multiple devices, where the content is periodically synchronized between the devices and the storage service. Embodiments are directed to detection of bulk operations associated with content stored at a storage service to protect users both proactively and remedially from accidental and/or nefarious content alterations propagating to the storage service and/or other devices. A model may be created based on a usage pattern associated with the content, an attempted operation performed on the content may be detected, and the attempted operation may be compared to the model to determine whether the operation deviates from the usage pattern. If the attempted operation deviates from the usage pattern, a preservation policy for the content at the storage service may be modified and a notification of the attempted operation may be provided to obtain user approval.