Telemetry Signal Randomization for Business Activity Camouflage
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Solution Overview
Problem
Telemetry signals from enterprise servers contain business-activity information that can be inferred by competitors or unauthorized individuals, posing serious business risks if accessed, as existing technologies do not provide adequate confidentiality for these sensitive data.
Innovation Solution
A system that monitors telemetry signals, computes serial correlation, performs frequency domain analysis, and generates artificial activity to reduce serial correlation, effectively camouflaging business-activity information by introducing artificial load impulses at specific frequencies, thereby randomizing the time series and masking business dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If telemetry signals are collected and monitored to track business activities, then operational visibility and performance monitoring are improved, but business security and confidentiality deteriorate due to exposure of sensitive business-activity information
Solution Approach 1:
The patent introduces an intermediary processing layer between the telemetry collection system and external analysis. This intermediary applies randomization techniques to mask business-activity information while preserving operational patterns, effectively mediating between the need for monitoring and the need for security.
Solution Approach 2:
The patent transforms telemetry data by changing its statistical parameters through randomization. By modifying the temporal and distributional characteristics of the data while preserving underlying patterns, the system maintains measurement utility while eliminating direct business information exposure.
2Object-affected harmful factors
If artificial activity is generated to reduce serial correlation in telemetry data, then business-activity information is camouflaged, but system complexity and processing requirements increase
Solution Approach 1:
The patent employs periodic randomization patterns that introduce artificial activity at specific intervals. This periodic approach systematically reduces serial correlation by injecting controlled variations at regular intervals, making the masking process more manageable and less computationally intensive than arbitrary randomization.
Solution Approach 2:
The system incorporates feedback mechanisms to monitor serial correlation levels and adjust the artificial activity generation accordingly. By continuously measuring the effectiveness of the randomization and adapting the artificial activity injection, the system optimizes the balance between security and processing complexity.
Data Source
AI summary
One embodiment of the present invention provides a system that camouflages business-activity information in telemetry signals from a computer system. During operation, the system monitors telemetry signals from the computer system to obtain a time series containing a telemetry metric which provides business-activity information. Next, the system computes a serial correlation between data values in the time series. The system then determines if the computed serial correlation between the data values in the time series is above a predetermined threshold level. If so, the system performs frequency domain analysis on the time series. The system then generates artificial activity on the computer system which causes the frequency spectra of the time series to reduce the serial correlation between the data values in the time series.