Aggregated Data Anonymization via Segmented Mapping
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Solution Overview
Problem
Existing data anonymization methods for protecting user privacy require knowledge of the actual state of power-consuming devices, which is economically infeasible and computationally prohibitive, especially when dealing with large amounts of time-series data needed to maintain analytical usefulness.
Innovation Solution
A method that partitions aggregated data into segments, assuming stationarity across different time durations, allowing for the same mapping to be applied across segments, thereby reducing the need for knowledge of actual device states and simplifying computational burdens, while preserving privacy and analytical usefulness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data anonymization methods use actual device states to protect privacy, then privacy protection is improved, but device complexity and economic feasibility deteriorate due to requiring sensors on each device
Solution Approach 1:
The patent extracts and removes the requirement for actual device state knowledge from the anonymization process. Instead of using sensors to capture real device states, the method uses only aggregated power consumption data, thereby eliminating the need for complex sensor deployment while maintaining privacy protection through mathematical transformations of the aggregated data
Solution Approach 2:
The patent introduces aggregated power consumption data as an intermediary between the raw device states and the anonymization process. This intermediary representation allows privacy protection to be achieved through processing the aggregated data without needing access to the actual individual device states, thus avoiding the complexity of direct device monitoring
2Loss of information
If conventional anonymization methods process large amounts of time-series data, then analytical usefulness is improved, but computational burden increases to prohibitive levels
Solution Approach 1:
The patent applies preliminary action by performing anonymization transformations on smaller segments of time-series data as they become available, rather than waiting to process the entire large dataset at once. This approach maintains analytical usefulness by preserving the temporal structure and patterns in the data while reducing computational burden through incremental processing
Solution Approach 2:
The patent changes the parameter of data granularity by working with segmented portions of time-series data and applying anonymization transformations that preserve essential statistical properties. This allows the system to maintain analytical usefulness through preserved data patterns while reducing computational burden by processing data in manageable segments with optimized transformation algorithms
3Measurement precision
If sensors are connected to each power-consuming device, then knowledge of actual device states is obtained, but economic feasibility deteriorates due to high deployment costs
Solution Approach 1:
The patent uses aggregated power consumption data as a copy or representation of the underlying device states without needing direct sensor access to each device. This copied representation contains sufficient information for anonymization purposes while avoiding the high costs of deploying and maintaining sensors on every individual device
Solution Approach 2:
The patent makes the anonymization method universal by designing it to work with aggregated data from any source without requiring device-specific sensors. This multi-functional approach allows the same anonymization technique to be applied across different devices and settings, eliminating the need for expensive device-specific measurement infrastructure
Data Source
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AI summary
Methods and systems for transmitting user aggregate data to a third party, such that a privacy of the aggregated data is protected, while analytical usefulness of the aggregated data is preserved. The method including receiving, using a transceiver, aggregated data including time-series data collected over a period of time. Selecting, from a memory, a mapping for transforming a segment of the aggregated data of a predetermined size. Partitioning the aggregated data into a multiple data segments, each data segment is of the predetermined size. Transforming each data segment using the mapping to produce multiple transformed data segments, wherein each data segment is transformed by the mapping independently from other data segments. Finally, transmitting, using the transceiver, the multiple transformed data segments to a third party over a communication channel, wherein steps of the method are performed by a processor operatively connected with the memory and the transceiver.