Poisoned Data Curation With Aggregate Anomaly Screening
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Solution Overview
Problem
Existing data curation methods fail to effectively detect and mitigate large-scale anomalous data, particularly poisoned data, leading to increased false positives and resource misallocation due to inefficient anomaly detection in complex datasets.
Innovation Solution
Implementing a system that performs anomaly detection at an aggregate level, followed by false positive detection using synthetic datasets and optimization methods to identify and remediate poisoned data portions, ensuring reliable data provision to downstream consumers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anomaly detection is performed on individual data points in complex datasets, then detection granularity is improved, but false positive rate increases and resource efficiency deteriorates
Solution Approach 1:
The patent segments the anomaly detection process into two distinct levels: (1) aggregate-level anomaly detection that operates on summary statistics of data batches to identify potential poisoned data sources, and (2) individual data point analysis that is triggered only for suspected anomalies. This segmentation allows the system to maintain high detection granularity where needed while avoiding exhaustive analysis of all data points, thereby resolving the contradiction between detection precision and processing efficiency.
Solution Approach 2:
The patent implements partial action by performing comprehensive anomaly detection only on a subset of data points that are flagged as potential anomalies at the aggregate level. Instead of analyzing every individual data point in the dataset, the system applies detailed examination only where necessary, reducing computational resources wasted on normal data while maintaining detection effectiveness for poisoned data.
2Productivity
If aggregate-level anomaly detection is used to improve processing efficiency, then productivity is improved, but measurement precision deteriorates due to increased false positives
Solution Approach 1:
The patent implements a feedback mechanism where aggregate-level anomaly detection results trigger subsequent individual data point analysis. When aggregate statistics indicate potential anomalies, the system feeds back to perform detailed examination of specific data points from the suspected sources. This feedback loop allows the system to maintain high processing efficiency at the aggregate level while correcting false positives through targeted individual analysis, thereby improving measurement precision without sacrificing productivity.
3Reliability
If traditional anomaly detection methods are applied to poisoned data, then detection coverage is improved, but resource allocation deteriorates due to misidentification of false positives
Solution Approach 1:
The patent performs preliminary anomaly detection at the aggregate level before investing computational resources in individual data point analysis. By first identifying suspicious data sources through aggregate statistics, the system prepares a targeted list of candidates for detailed examination. This preliminary action ensures that comprehensive detection coverage is achieved only for high-priority suspects, preventing waste of computational energy on exhaustive analysis of all data points while maintaining reliable detection of poisoned data.
Data Source
AI summary
Methods and systems for curating data by a data manager are disclosed. Data may be curated from various data sources before being supplied to downstream consumers, and the downstream consumers may rely on the trustworthiness of the curated data to facilitate desired computer-implemented services. Therefore, to identify data (e.g., poisoned data) that, if provided to downstream consumers may negatively impact the desired computer-implemented services, the data may undergo anomaly detection. Data anomalies indicating the presence of poisoned data may be identified based on anomaly patterns exhibited by the data. However, an anomaly pattern may be likely to appear by chance (e.g., may incorrectly indicate the presence of poisoned data) in large volumes of complex data. Therefore, anomaly detection may be coupled with false positive detection in order to manage false positive anomalies. When a true positive anomaly (e.g., poisoned data) is detected, the poisoned portion may be identified and/or remediated.