Verifiable Service Provider Performance for Query Response Provision
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Solution Overview
Problem
Data owners face challenges in ensuring privacy and accuracy when using a service provider for data management, as they cannot guarantee that the service provider is fulfilling privacy requirements or providing accurate responses, especially when relying on a semi-trusted provider.
Innovation Solution
A system where data owners set privacy budgets for their data, allowing them to control the level of privacy and noise added to responses, with the service provider aggregating responses and generating proofs for verification, ensuring privacy and accuracy through hashing and noise management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data owners use a service provider for data management, then cost and complexity are reduced, but privacy and accuracy cannot be guaranteed
Solution Approach 1:
The patent introduces a trusted execution environment (TEE) as an intermediary between the data owner and service provider. The TEE acts as a secure mediator that verifies privacy budget compliance and ensures accurate query responses without requiring the data owner to directly trust the service provider, thus reducing management complexity while maintaining privacy guarantees
Solution Approach 2:
The system implements a feedback mechanism where the TEE continuously monitors and verifies that the service provider adheres to the specified privacy budget. This feedback loop provides cryptographic proofs of compliance, allowing data owners to verify privacy and accuracy requirements are met while still benefiting from outsourced data management
2Reliability
If noise is added to responses to ensure privacy, then privacy is improved, but response accuracy deteriorates
Solution Approach 1:
The patent applies differential privacy selectively based on local data sensitivity and query requirements. Instead of uniformly adding noise to all responses, the system dynamically determines the appropriate noise level for each specific query and data set, ensuring privacy compliance while minimizing the impact on response accuracy where possible
Solution Approach 2:
The system dynamically adjusts the noise parameter based on the privacy budget and query characteristics. By changing the noise level parameter adaptively rather than using a fixed value, the system can satisfy privacy requirements while maintaining optimal response accuracy for different types of queries and data sensitivity levels
3Reliability
If verification mechanisms are implemented to ensure privacy and accuracy, then reliability is improved, but system complexity increases
Solution Approach 1:
The verification mechanism is implemented within the trusted execution environment itself, which automatically performs privacy budget verification and generates cryptographic proofs. This self-service approach eliminates the need for external verification infrastructure, as the TEE inherently provides the verification functionality without adding external system complexity
Solution Approach 2:
The system uses cryptographic copies and hashes of the privacy budget and query parameters for verification purposes. Instead of storing and comparing full datasets, the TEE works with compact cryptographic representations, significantly reducing the complexity of verification operations while maintaining security and accuracy
Data Source
AI summary
One embodiment provides a computer implemented method, including: receiving, at a service provider from each of a plurality of data owners each having data accessible to the service provider, a privacy budget, wherein the privacy budget identifies a set of privacy requirements to be employed by the service provider on data of the data owner; receiving, at the service provider, a query from a client; receiving, at the service provider from each of at least a subset of the data owners, a response to the query; adding, by the service provider, noise to each of the responses, wherein an amount of noise added to each response is based upon the privacy budget of the data owner corresponding to a given response; and returning, by the service provider to the client, an aggregated response including the responses having added noise.