What can you do with Vistara?
Let's look at how each approach can integrate with Vistara:
Type 1 Verifiability:
Implement foundational security measures to establish initial trust.
Type 2 Verifiability:
Ensure that all data within the network is tamper-proof. Enhance reliability and confidentiality.
Type 3 Verifiability:
Protect sensitive computations and data.
Type 4 Verifiability:
Guarantee the correctness of critical computations through distributed consensus.
Type 5 Verifiability:
Ensure reproducibility and verifiability of results.
Let's refine it.
In terms of Verifiability in Vistara's decentralized network, let's define them in categories based on their complexity, scope, and security guarantees.
Type 1: Basic Integrity and Trust Establishment
Secure Boot and Remote Attestation:
Secure Boot: Ensures the hardware and initial software states are trusted by verifying the integrity of the boot process.
Remote Attestation: Verifies the integrity of the running software stack through attestation reports, providing a foundation of trust.
Type 2: Data Integrity and Confidential Computation
Cryptographic Techniques:
Merkle Trees: Provide tamper-proof data structures that ensure data integrity.
Zero-Knowledge Proofs (ZKPs): Enable confidential computations by allowing verification without revealing underlying data.
Type 3: Redundancy and Confidential Data Processing
Replication of Computation:
Multi-Party Computation (MPC): Facilitates confidential data processing by distributing computation tasks across multiple parties without revealing inputs.
Redundant Computation: Enhances reliability by performing the same computation on multiple nodes and comparing results.
Type 4: Consensus and Distributed Verification
Consensus-Based Verification:
Consensus Mechanisms: Ensure the correctness of computation results through mechanisms like Byzantine Fault Tolerance, which rely on agreement among nodes.
Type 5: Deterministic and Reproducible Computation
Deterministic Computation:
Reproducible Results: Guarantees that computations produce the same output given the same input and initial state, enabling easy verification across nodes.
So what? Let's see how to implement this in the next section.
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