Differentially Private Top-K Selection via Domain Segmentation
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Solution Overview
Problem
Existing methods for ensuring differential privacy in top-k selection from massive datasets are inefficient and impractical, especially when the domain is dynamic or large, as they often require knowing the entire domain or building additional structures, which can be impractical and resource-intensive.
Innovation Solution
A system and method that adds noise to the counts of data items to generate noisy counts and thresholds, allowing for differential privacy without requiring knowledge of the entire domain, and can operate in real-time by preprocessing data to access only subsets, using techniques like Gumbel or Laplace noise generation based on sensitivity settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If querying the count of every domain element is performed to implement differential privacy, then privacy guarantees are achieved, but the requirement to know the entire domain becomes impractical when the domain is dynamic or large
Solution Approach 1:
The patent segments the domain into multiple partitions or buckets, where each partition contains a subset of domain elements. Instead of querying all elements, the system queries only the relevant partitions that may contain the top-k elements. This segmentation allows differential privacy to be maintained while avoiding the need to know or query the entire domain, thus resolving the contradiction between privacy guarantees and ease of operation.
Solution Approach 2:
The patent applies partial action by querying only a subset of domain elements (specifically, only those in relevant partitions) rather than all elements. By using partitioning and pruning techniques, the system performs differential privacy queries on a partial set of elements that are sufficient to determine the top-k results, thereby reducing the operational burden while maintaining privacy guarantees.
2Reliability
If additional structure is built on top of a large data domain to implement differential privacy, then privacy guarantees are achieved, but significant processing time and extra storage are required
Solution Approach 1:
The patent segments the large data domain into smaller partitions, which reduces the amount of data that needs to be processed and stored for each partition. This segmentation allows the system to build and maintain differential privacy structures more efficiently, as each partition requires less processing time and storage compared to the entire domain, thus resolving the contradiction between privacy guarantees and productivity.
Solution Approach 2:
The patent applies partial action by maintaining differential privacy structures only for relevant partitions rather than the entire domain. This approach reduces the overall processing time and storage requirements, as the system only needs to maintain and query structures for partitions that may contain top-k elements, thereby improving productivity while maintaining privacy guarantees.
3Reliability
If additional structure is built on top of a large data domain to implement differential privacy, then privacy guarantees are achieved, but extra storage is required
Solution Approach 1:
The patent segments the domain into partitions, which allows the storage requirements for differential privacy structures to be distributed across multiple smaller units. Each partition requires less storage space than the entire domain, and the system can selectively load and process only relevant partitions, thereby reducing the total storage footprint while maintaining privacy guarantees.
Solution Approach 2:
The patent applies partial action by storing and maintaining differential privacy structures only for relevant partitions rather than the entire domain. This approach significantly reduces the storage space required, as the system only maintains structures for partitions that may contain top-k elements, thereby resolving the contradiction between privacy guarantees and storage requirements.
Data Source
AI summary
Techniques for ensuring differential privacy in top-K selection are provided. In one technique, multiple items and multiple counts are identified in response to a query. For each count, which corresponds to a different item, a noise value is generated and added to the count to generate a noisy value, and the noisy value is added to a set of noisy values that is initially empty. A particular noise value is generated for a particular count and added to the particular count to generate a noisy threshold. The particular noise value is generated using a different technique than the technique used to generate each noise value in the set. Based on the noisy threshold, a subset of the noisy values is identified, where each noisy value in the subset is less than the noisy threshold. A response to the query is generated that excludes items that correspond to the subset.