đģHigh Level View
Data Verification Request (DVR) is the main zkPass module running within the framework of the zkPass Service.
Last updated
Data Verification Request (DVR) is the main zkPass module running within the framework of the zkPass Service.
Last updated
The DVR application facilitates the verification of user data while maintaining its privacy. To use the DVR and participate in the DVR workflow, the DVR clients take one of the following client roles: Data Issuer, Data Holder (the user), and Proof Verifier.
The Proof Verifier sets the criteria for users to access certain services or resources. These criteria are encoded as a query script written by the Proof Verifier client, and the script represents business logic that verifies against the user's documents.
Although the user owns their data, it is typically issued and signed by a trusted entity known as the Data Issuer. The zkPass service processes the business logic specified in the DVR query script, generating a Zero Knowledge proof and the query output. The Proof Verifier then uses this proof and output to determine if the user meets the specified criteria. Throughout this process, the userâs data is never transmitted to the Proof Verifier, ensuring its privacy. The Verifier only receives the proof and the relevant output.
The high-level view of the DVR application and its clients is illustrated here.
It should be noted that with this server-centric approach, the user's sensitive input data is no longer confined to their device but is instead processed in a centralized server. This shift necessitates a degree of trust to ensure the confidentiality of the user's input. To alleviate this concern, zkPass Service runs the zkPass module, such as the DVR, within a Trusted Execution Environment (TEE). This secure and isolated environment aims to provide an additional layer of protection, safeguarding the privacy of user data while allowing for the efficient execution of DVR queries. In other words, zkPass can still protect data confidentiality through the trusted privacy model.
The diagram of the DVR for the holder-centric workflow is depicted below.
Beyond merely transferring the computational load of proof generation from the client device to the server, DVR incorporates a robust JSON-based query language (DVR Query) that empowers developers to articulate specific requirements or conditions to be applied to the user's data. This JSON query is executed within the Zero-Knowledge Virtual Machine (ZKVM), resulting in a cryptographic proof that verifies the execution has occurred exactly as intended, without any alterations.
The DVR inherits the two pivotal features from its client-side predecessor, enhancing them within its server-centric architecture:
Trusted Data Privacy By generating a cryptographic proof, DVR ensures that the underlying data remains confidential. The Proof Verifier can confirm the proof's validity without ever needing to access the original sensitive data. While the service-based approach does introduce a new set of concerns, which is the need to trust a centralized server with sensitive user data, DVR mitigates this issue by operating within a TEE. This adds an extra layer of security to maintain data integrity and confidentiality in an isolated and secure centralized computer.
Query Execution Transparency The cryptographic proof generated by ZKVM serves as an immutable record that the query has been executed faithfully, meeting all specified conditions or requirements. Unlike often rigid proof functions found in client-side implementations, DVR offers an easy-to-use JSON query language. This allows users to effortlessly adjust to varying logic to enforce data requirements or conditions.
This advanced feature set not only alleviates computational constraints but also offers a highly flexible, secure, and transparent way for developers to implement privacy-centric logic within their applications.
Transitioning from a client-centric to a service-oriented proof system, the DVR application and the zkPass Service strive to deliver a scalable, efficient framework that accommodates a wide range of devices with diverse computational resources. It does so without compromising its commitment to data privacy and transparent execution. Moreover, it incorporates a versatile JSON-based DVR query language that is adaptable to any user data schema, offering users even greater flexibility.
