The Visible Protocol:
Decentralizing Personal Context.
A protocol for individuals to own their digital footprint and for organizations to access aggregated insight without handling personal data.
Abstract
The current digital economy relies on a "surveillance model" where user data is harvested, centralized, and sold. This creates privacy risks for individuals and liability silos for organizations. Visible proposes an alternative: a "verified context model" where data remains local and self-sovereign.
By decoupling identity from insight using Zero-Knowledge Proofs (ZKPs) and a local-first graph architecture, Visible creates a market for insight where privacy is the default. The VSBL token serves as the utility currency for computation, storage, and incentive alignment. This paper outlines the protocol architecture, the privacy mechanisms, and the economic model that sustains it.
1. Introduction
1.1 The Privacy-Utility Gap
Historically, digital utility has come at the cost of privacy. To get personalized recommendations or relevant offers, users must surrender their data to centralized aggregators. These aggregators become single points of failure and prime targets for breaches. For businesses, "knowing your customer" has morphed into "surveilling your customer," creating regulatory liability and eroding trust.
This tradeoff is not inherent to technology; it is an artifact of centralized architecture. When data must be moved to be processed, it must be trusted to the processor. This creates a "trust gap" that has been filled by legal contracts rather than cryptographic guarantees.
1.2 The Visible Solution: Compute-over-Data
Visible inverts this model. Instead of bringing user data to the algorithm, we bring the algorithm to the user's data. Individuals aggregate their own digital footprint into a secure, local-first Personal Graph. When an organization needs insight, they post a query to the network. User nodes process this query locally and return a cryptographic proof of the result—not the raw data.
This "Compute-over-Data" approach ensures that raw personal information never leaves the user's control, yet organizations still receive the verified aggregate insights they need. The result is a system where privacy is not a policy choice but an architectural guarantee.
2. Architecture
The Visible Protocol is built on a modular stack designed to separate data storage, computation, and consensus. This separation allows the network to scale while maintaining strict privacy boundaries.
The Personal Graph
A local, encrypted graph database residing on the user's device (or encrypted cloud storage). It ingests data from public sources (social media, blockchains) and private sources (email, browsing history) via user-authorized connectors.
Key Tech: SQLite/EdgeDB, AES-256 Encryption, DID (Decentralized Identifiers).
Zero-Knowledge Proofs
A cryptographic method allowing a user to prove a statement is true (e.g., "I am over 18" or "I live in Boston") without revealing the underlying data. This is the core verification primitive that allows "trustless" insight.
Key Tech: zk-SNARKs, Circom circuits.
2.1 Network Participants
These nodes hold the Personal Graph. They are responsible for data ingestion, local storage, and generating ZK proofs. Owners stake VSBL to prevent spam and ensure honest participation. They never broadcast raw data, only proofs.
These nodes post queries (bounties) to the network. They specify the criteria for respondents (e.g., "Must have >500 followers") and the question to be answered. They pay VSBL tokens to fund the bounty pool.
Validators maintain the integrity of the network. They check the validity of ZK proofs submitted by Owners. If a proof is valid, they authorize the release of rewards from the bounty smart contract. Validators stake VSBL to participate and are slashed for malicious behavior.
2.2 The Protocol Stack
Layer 3: Application
The interface layer where Seekers (organizations) define queries and view aggregated results. It includes the marketplace for data bounties, dashboards for visualizing insights, and wallet integrations for managing VSBL tokens. This layer abstracts away the complexity of ZK proofs and cryptographic primitives.
Layer 2: Coordination
The protocol logic responsible for matching open bounties with eligible Owner nodes. It handles the recursive aggregation of individual proofs into a single, compact validity proof. This layer also manages the settlement of VSBL payments, ensuring that rewards are released only when proofs are verified on-chain.
Layer 1: Data & Compute
The foundation of the protocol, running locally on user devices. It consists of the Personal Graph (local encrypted storage) and the ZK Circuit Execution Engine. This layer performs the heavy lifting of data ingestion, normalization, and proof generation without data ever leaving the device.
3. The VSBL Token
VSBL is a utility token that aligns incentives across the network. It functions as both a unit of computation (like "gas") and a medium of reward transfer. It ensures the system is funded by usage rather than data monetization.
3.1 Token Utility (Inputs)
Tokens are consumed to power the infrastructure. This creates a sustainable base demand for the token correlated with network activity. Unlike ad-supported models, where the user is the product, here the user pays (via tokens) for the utility of privacy and storage, often subsidized by the earnings they generate.
Staking
To participate in the network, both Owner nodes and Validators must stake VSBL. This aligns economic incentives: participants put capital at risk to guarantee their honest behavior. Slashing conditions apply for malicious actions like spamming invalid proofs or incorrectly validating them.
Storage
The network requires tokens to pay for the encrypted backup and state management of Personal Graphs. While data is local-first, encrypted backups ensure durability across devices. This cost scales with graph size, incentivizing efficient data management.
Compute
Generating Zero-Knowledge Proofs is computationally intensive. Tokens are used to pay for the "gas" required to execute ZK circuits locally or via a trusted delegate. This ensures that the network is compensated for the computational resources consumed during verification.
Ingest
Connectors to external platforms (e.g., Twitter, LinkedIn, HealthKit) require maintenance and API bandwidth. Tokens fund the operation of these secure bridges, allowing users to import verified data points into their Personal Graph.
3.2 Incentive Layer (Outputs)
Tokens are distributed to incentivize participation and data quality. This creates a circular economy where businesses buy tokens to fund research, and individuals earn tokens to fund their digital existence.
Bounties: Pricing Verified Context
When a Seeker posts a question, they attach a bounty pool in VSBL. This pool is distributed to Owners who provide valid, verified responses. This mechanism prices "verified attention" and "verified insight."
Quality Multipliers
Bounties can scale based on the "richness" of the proof. A user proving they are a "Verified CTO" might earn 10x more than a generic respondent. This encourages users to curate high-quality Personal Graphs.
Staking for Honesty
Both Owners and Validators must stake VSBL to participate. If they spam the network or generate invalid proofs, their stake is slashed. This economically guarantees integrity and prevents bad actors.
4. Economics & Scalability
This section starts with concrete market primitives (what a Seeker funds and what an Owner submits), then shows an end-to-end cost model, and finally explains why the protocol scales (succinct verification and proof aggregation).
4.1 Market examples (Demand vs. Supply)
Each example shows both sides at once: the Seeker’s question + bounty + eligibility requirements, and the Owner’s answer + proof + stake + reward.
Example A: Employer pulse survey
What an Owner submits
Example B: Product research (verified cohort)
What an Owner submits
4.2 Full cost model (staking + bounty + rewards + gas)
Below is a complete worked example showing (a) how much the Seeker funds, (b) how much Owners can earn, (c) how staking works (locked vs spent), and (d) how gas costs are computed.
Notes: staking is a safety mechanism (anti-Sybil/anti-spam) and is typically returned after a challenge window unless slashed. Gas values and prices are illustrative; real costs vary by chain and implementation.
4.3 Computational scalability
A core property of ZK-SNARKs is that verification time is succinct (nearly constant) regardless of the complexity of the underlying computation. This allows the Visible Protocol to verify proofs about massive datasets (e.g. “scan 10 years of email history for a property”) on-chain for a trivial, predictable cost.
Where N is the size of the dataset. This decoupling of data volume from verification cost is what makes “Compute-over-Data” economically viable at scale.
Verification Cost vs. Data Scale
As dataset size grows (linear), verification cost remains nearly constant (logarithmic).
4.4 Proof aggregation: scaling participation
Proof aggregation reduces the cost of verifying many individual contributions. Instead of verifying \(N\) proofs (one per participant), the network aggregates them into a single succinct proof that attests “all \(N\) proofs were valid”, then settles once.
Proof aggregation reduces settlement cost
Individual verification grows with participants; aggregated verification stays nearly constant.
Chart values are illustrative to show the shape: individual verification grows linearly with \(N\), while aggregated verification stays close to constant.
5. Security & Privacy Analysis
Threat Model
Visible assumes an "Honest-but-Curious" threat model for the network coordination layer. We assume that Seekers and Validators may want to learn the identity of Owners, but they are constrained by the cryptographic properties of the protocol.
Privacy Guarantees
- •Data MinimizationOnly the final boolean result (True/False) or aggregate statistic is ever revealed. The raw data needed to compute it remains on the Owner's device.
- •UnlinkabilityProofs are submitted with ephemeral identifiers. A Seeker cannot link two responses from the same user across different bounties unless the user explicitly opts into a persistent persona.
- •Proof AggregationIndividual proofs are recursively aggregated into a single validity proof before settlement. This hides the individual contribution pattern and scales the network.
- •Computational IntegrityZKPs guarantee that the output was generated correctly from the input data, preventing users from spoofing results without actually having the data.
6. Conclusion
The Visible Protocol represents a fundamental shift in the data economy. By using VSBL to price computation and verified insight, we create a sustainable model that does not rely on surveillance. Privacy becomes a property of the infrastructure, not a policy choice.