This document defines the comprehensive strategy for integrating the Evidence-Based Subjective Logic (EBSL) algorithm into the client-side prover infrastructure. It covers the technical approach for converting the PyTorch-based EBSL implementation to a zero-knowledge compatible format using EZKL, ensuring scalability for arbitrary network sizes, and implementing aggregate proofs.
Subjective Opinion Representation
interface SubjectiveOpinion {
belief: number; // b ∈ [0,1] - Positive evidence strength
disbelief: number; // d ∈ [0,1] - Negative evidence strength
uncertainty: number; // u ∈ [0,1] - Lack of evidence
base_rate: number; // a ∈ [0,1] - Prior probability
}
// Constraint: b + d + u = 1
// Expected value: E = b + a*uEBSL Fusion Rules
// Fusion of two opinions ω₁ and ω₂
function ebslFusion(ω₁: SubjectiveOpinion, ω₂: SubjectiveOpinion): SubjectiveOpinion {
const { b1, d1, u1, a1 } = ω₁;
const { b2, d2, u2, a2 } = ω₂;
// Denominator calculation with numerical stability
const denominator = u1 + u2 - (u1 * u2);
if (Math.abs(denominator) < EPSILON) {
// Handle degenerate case when both opinions are certain
return handleCertainOpinionsFusion(ω₁, ω₂);
}
// Standard EBSL fusion formulas
const b_fused = (b1 * u2 + b2 * u1) / denominator;
const d_fused = (d1 * u2 + d2 * u1) / denominator;
const u_fused = (u1 * u2) / denominator;
const a_fused = (a1 * u2 + a2 * u1) / denominator;
return {
belief: clamp(b_fused, 0, 1),
disbelief: clamp(d_fused, 0, 1),
uncertainty: clamp(u_fused, 0, 1),
base_rate: clamp(a_fused, 0, 1)
};
}Trust Path Aggregation
interface TrustPath {
source: string;
target: string;
path: string[];
opinions: SubjectiveOpinion[];
weights: number[];
confidence: number;
}
class TrustNetworkProcessor {
/**
* Compute reputation using multi-path EBSL aggregation
*/
computeNetworkReputation(
targetUser: string,
trustPaths: TrustPath[],
aggregationStrategy: AggregationStrategy
): ReputationResult {
// 1. Validate and filter trust paths
const validPaths = this.validateTrustPaths(trustPaths);
// 2. Compute path-level opinions
const pathOpinions = validPaths.map((path) => this.computePathOpinion(path));
// 3. Apply path weighting strategy
const weightedOpinions = this.applyPathWeighting(pathOpinions, aggregationStrategy);
// 4. Perform EBSL fusion across all paths
const finalOpinion = this.fuseManyOpinions(weightedOpinions);
// 5. Convert to reputation score
const reputationScore = this.opinionToReputation(finalOpinion);
return {
user: targetUser,
score: reputationScore,
opinion: finalOpinion,
pathCount: validPaths.length,
confidence: this.computeConfidence(finalOpinion, validPaths),
metadata: {
algorithm: "EBSL-Network-v1.0",
timestamp: Date.now(),
networkSize: trustPaths.length,
},
};
}
private computePathOpinion(path: TrustPath): SubjectiveOpinion {
// Sequential fusion along trust path
let pathOpinion = path.opinions[0];
for (let i = 1; i < path.opinions.length; i++) {
// Apply path decay and distance weighting
const decayedOpinion = this.applyPathDecay(path.opinions[i], i, path.weights[i]);
pathOpinion = ebslFusion(pathOpinion, decayedOpinion);
}
return pathOpinion;
}
}Important: This implementation follows the proven PyTorch → ONNX → EZKL pipeline as demonstrated in Notebooks/ebsl_full_script.py and EBSL_Pipeline_Complete.ipynb, NOT Circom circuits.
The notebook already provides a complete EZKL integration. We build upon this foundation:
# From notebook: EBSLFusionModule already implemented
class EBSLFusionModule(nn.Module):
"""
PyTorch module for EBSL fusion compatible with EZKL compilation
Already implemented and tested in the notebook
"""
def __init__(self, max_opinions: int = 16, precision_bits: int = 32):
super().__init__()
self.max_opinions = max_opinions
self.scale_factor = 2 ** (precision_bits - 8)
self.epsilon = torch.tensor(1.0 / self.scale_factor)
self.one = torch.tensor(1.0)
def forward(self, opinions_tensor: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
# Implementation already proven in notebook
# Uses overflow-safe operations for ZK compatibility
return fused_opinionApproach: Leverage the notebook's proven EZKL pipeline for client-side use:
class LocalEZKLProver {
/**
* Generate ZK proof using the notebook's proven EZKL pipeline
* Runs entirely locally in the client without backend dependencies
*/
async generateLocalProof(
userAttestations: TrustAttestation[]
): Promise<LocalZKProof> {
// 1. Use local EBSL computation (already implemented)
const reputation = this.localEBSL.computeReputation(userAddress, userAttestations);
// 2. Prepare data in ONNX format (matching notebook)
const onnxInput = this.prepareONNXInput(userAttestations);
// 3. Use pre-compiled EZKL circuit (from notebook pipeline)
const proof = await this.ezkl.generateProof(onnxInput, this.precompiledCircuit);
return {
reputation: reputation.score,
proof: proof,
generated_locally: true,
computation_method: 'EZKL-ONNX-PyTorch'
};
}
private prepareONNXInput(attestations: TrustAttestation[]): ONNXInput {
// Format data exactly as expected by notebook's PyTorch model
const maxOpinions = 16;
const opinions = Array(maxOpinions).fill([0, 0, 1, 0.5]);
const mask = Array(maxOpinions).fill(false);
attestations.slice(0, maxOpinions).forEach((att, i) => {
opinions[i] = [
att.opinion.belief,
att.opinion.disbelief,
att.opinion.uncertainty,
att.opinion.base_rate
];
mask[i] = true;
});
return { opinions, mask };
}
}
**Precision Management**
```typescript
class ZKEBSLProcessor {
private readonly PRECISION_BITS = 32;
private readonly SCALE_FACTOR = 2 ** (this.PRECISION_BITS - 8);
private readonly EPSILON = 1 / this.SCALE_FACTOR;
/**
* Convert floating point opinion to fixed-point representation
*/
encodeOpinion(opinion: SubjectiveOpinion): FixedPointOpinion {
return {
belief: Math.round(opinion.belief * this.SCALE_FACTOR),
disbelief: Math.round(opinion.disbelief * this.SCALE_FACTOR),
uncertainty: Math.round(opinion.uncertainty * this.SCALE_FACTOR),
base_rate: Math.round(opinion.base_rate * this.SCALE_FACTOR)
};
}
/**
* ZK-safe fusion with overflow protection
*/
zkSafeFusion(
op1: FixedPointOpinion,
op2: FixedPointOpinion
): FixedPointOpinion {
const { b1, d1, u1, a1 } = op1;
const { b2, d2, u2, a2 } = op2;
// Compute denominator with overflow checking
const denom_raw = u1 + u2 - this.fixedPointMultiply(u1, u2);
const denominator = Math.max(denom_raw, this.EPSILON * this.SCALE_FACTOR);
// Perform division with rounding
const b_fused = this.fixedPointDivide(
this.fixedPointMultiply(b1, u2) + this.fixedPointMultiply(b2, u1),
denominator
);
const d_fused = this.fixedPointDivide(
this.fixedPointMultiply(d1, u2) + this.fixedPointMultiply(d2, u1),
denominator
);
const u_fused = this.fixedPointDivide(
this.fixedPointMultiply(u1, u2),
denominator
);
const a_fused = this.fixedPointDivide(
this.fixedPointMultiply(a1, u2) + this.fixedPointMultiply(a2, u1),
denominator
);
return {
belief: this.clampFixed(b_fused),
disbelief: this.clampFixed(d_fused),
uncertainty: this.clampFixed(u_fused),
base_rate: this.clampFixed(a_fused)
};
}
private fixedPointMultiply(a: number, b: number): number {
// Multiply with overflow protection
const result = (a * b) / this.SCALE_FACTOR;
return Math.min(result, this.SCALE_FACTOR); // Clamp to max value
}
private fixedPointDivide(a: number, b: number): number {
// Division with precision preservation
return Math.round((a * this.SCALE_FACTOR) / b);
}
}Model Architecture
import torch
import torch.nn as nn
import ezkl
class EBSLFusionModule(nn.Module):
"""
PyTorch module for EBSL fusion compatible with EZKL compilation
"""
def __init__(self, max_opinions: int = 16, precision_bits: int = 32):
super().__init__()
self.max_opinions = max_opinions
self.scale_factor = 2 ** (precision_bits - 8)
self.epsilon = torch.tensor(1.0 / self.scale_factor)
self.one = torch.tensor(1.0)
def forward(self, opinions_tensor: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""
Forward pass for EBSL fusion
Args:
opinions_tensor: [batch_size, max_opinions, 4] - [b,d,u,a] values
mask: [batch_size, max_opinions] - boolean mask for valid opinions
Returns:
[batch_size, 4] - fused opinion [b,d,u,a]
"""
batch_size = opinions_tensor.shape[0]
# Apply mask to filter valid opinions
masked_opinions = opinions_tensor * mask.unsqueeze(-1)
valid_counts = torch.sum(mask, dim=1)
# Initialize with first valid opinion for each batch
fused_opinions = torch.zeros(batch_size, 4)
for batch_idx in range(batch_size):
valid_ops = masked_opinions[batch_idx][mask[batch_idx]]
if len(valid_ops) == 0:
# Default to uncertain opinion
fused_opinions[batch_idx] = torch.tensor([0.0, 0.0, 1.0, 0.5])
elif len(valid_ops) == 1:
fused_opinions[batch_idx] = valid_ops[0]
else:
# Sequential fusion
fused = valid_ops[0]
for i in range(1, len(valid_ops)):
fused = self.fuse_two_opinions(fused, valid_ops[i])
fused_opinions[batch_idx] = fused
return fused_opinions
def fuse_two_opinions(self, op1: torch.Tensor, op2: torch.Tensor) -> torch.Tensor:
"""
Fuse two opinions with numerical stability
"""
b1, d1, u1, a1 = op1[0], op1[1], op1[2], op1[3]
b2, d2, u2, a2 = op2[0], op2[1], op2[2], op2[3]
# Compute denominator with clamping
denom = u1 + u2 - (u1 * u2)
denom = torch.clamp(denom, min=self.epsilon)
# Compute fused components
b_fused = (b1 * u2 + b2 * u1) / denom
d_fused = (d1 * u2 + d2 * u1) / denom
u_fused = (u1 * u2) / denom
a_fused = (a1 * u2 + a2 * u1) / denom
# Clamp to valid ranges
return torch.stack([
torch.clamp(b_fused, 0.0, 1.0),
torch.clamp(d_fused, 0.0, 1.0),
torch.clamp(u_fused, 0.0, 1.0),
torch.clamp(a_fused, 0.0, 1.0)
])
class ReputationCalculator(nn.Module):
"""
Convert fused opinion to reputation score
"""
def forward(self, fused_opinion: torch.Tensor) -> torch.Tensor:
"""
Convert opinion to reputation score using expected value
Args:
fused_opinion: [batch_size, 4] - [b,d,u,a] values
Returns:
[batch_size, 1] - reputation scores
"""
belief = fused_opinion[:, 0]
uncertainty = fused_opinion[:, 2]
base_rate = fused_opinion[:, 3]
# Expected value calculation: E = b + a*u
reputation = belief + (base_rate * uncertainty)
return reputation.unsqueeze(-1)EZKL Compilation Pipeline (From Notebook)
class EZKLPipeline:
"""
Client-side EZKL pipeline based on proven notebook implementation
Enables local proof generation without backend dependencies
"""
def __init__(self, max_opinions: int = 16):
self.max_opinions = max_opinions
# Use the proven model architecture from notebook
self.model = EBSLFusionModule(max_opinions)
async def setupLocalCircuit(self, output_dir: str) -> LocalCircuitArtifacts:
"""
Set up EZKL circuit for local client use
Following the exact pipeline from ebsl_full_script.py
"""
# 1. Export PyTorch model to ONNX (notebook approach)
model = self.model
model.eval()
dummy_opinions = torch.randn(1, self.max_opinions, 4)
dummy_mask = torch.ones(1, self.max_opinions, dtype=torch.bool)
onnx_path = f"{output_dir}/ebsl_model.onnx"
torch.onnx.export(
model,
(dummy_opinions, dummy_mask),
onnx_path,
export_params=True,
opset_version=11, # Match notebook settings
do_constant_folding=True,
input_names=['opinions', 'mask'],
output_names=['fused_opinion']
)
# 2. Generate EZKL settings (notebook configuration)
settings_path = f"{output_dir}/settings.json"
await ezkl.gen_settings(onnx_path, settings_path, py_run_args={
"input_scales": [32, 32],
"param_scales": [32],
"scale_rebase_multiplier": 32,
"lookup_safety_margin": 2,
"num_inner_cols": 8
})
# 3. Use notebook's calibration approach
calibration_data = self.generateCalibrationData() # From notebook
await ezkl.calibrate_settings(calibration_data, onnx_path, settings_path)
# 4. Compile circuit for local use
circuit_path = f"{output_dir}/circuit.ezkl"
await ezkl.compile_circuit(onnx_path, settings_path, circuit_path)
# 5. Generate proving/verifying keys
pk_path = f"{output_dir}/proving_key.pk"
vk_path = f"{output_dir}/verifying_key.vk"
await ezkl.setup(circuit_path, pk_path, vk_path)
return LocalCircuitArtifacts({
circuit: circuit_path,
proving_key: pk_path,
verifying_key: vk_path,
onnx_model: onnx_path,
settings: settings_path
});
}def generate_calibration_data(self) -> str:
"""
Generate representative calibration data for EZKL
"""
calibration_samples = []
for _ in range(100): # Generate 100 calibration samples
# Random opinions with realistic distributions
opinions = torch.rand(1, self.max_opinions, 4)
# Normalize to satisfy b + d + u = 1 constraint
for i in range(self.max_opinions):
total = opinions[0, i, 0] + opinions[0, i, 1] + opinions[0, i, 2]
if total > 0:
opinions[0, i, :3] = opinions[0, i, :3] / total
else:
opinions[0, i] = torch.tensor([0.0, 0.0, 1.0, 0.5])
# Random mask for variable opinion counts
mask = torch.rand(1, self.max_opinions) > 0.3
calibration_samples.append({
"input_data": [opinions.tolist(), mask.tolist()]
})
calibration_path = "calibration_data.json"
with open(calibration_path, 'w') as f:
json.dump(calibration_samples, f)
return calibration_path
### 3. Scalability Architecture
#### 3.1 Network Size Handling
**Dynamic Circuit Selection**
```typescript
interface CircuitSpec {
maxOpinions: number;
maxDepth: number;
circuitPath: string;
provingKeyPath: string;
verifyingKeyPath: string;
estimatedProvingTime: number;
memoryRequirement: number;
}
class ScalableEBSLProver {
private circuitSpecs: Map<string, CircuitSpec> = new Map([
["small", {
maxOpinions: 10,
maxDepth: 2,
circuitPath: "circuits/ebsl_small.ezkl",
provingKeyPath: "keys/small.pk",
verifyingKeyPath: "keys/small.vk",
estimatedProvingTime: 15000, // 15 seconds
memoryRequirement: 2048 // 2GB
}],
["medium", {
maxOpinions: 50,
maxDepth: 3,
circuitPath: "circuits/ebsl_medium.ezkl",
provingKeyPath: "keys/medium.pk",
verifyingKeyPath: "keys/medium.vk",
estimatedProvingTime: 60000, // 1 minute
memoryRequirement: 8192 // 8GB
}],
["large", {
maxOpinions: 200,
maxDepth: 4,
circuitPath: "circuits/ebsl_large.ezkl",
provingKeyPath: "keys/large.pk",
verifyingKeyPath: "keys/large.vk",
estimatedProvingTime: 300000, // 5 minutes
memoryRequirement: 32768 // 32GB
}]
]);
selectOptimalCircuit(userTrustData: TrustNetworkData): string {
const opinionCount = userTrustData.attestations.length;
const networkDepth = userTrustData.maxPathLength;
const availableMemory = this.getAvailableMemory();
// Find smallest circuit that can handle the data
for (const [size, spec] of this.circuitSpecs) {
if (opinionCount <= spec.maxOpinions &&
networkDepth <= spec.maxDepth &&
availableMemory >= spec.memoryRequirement) {
return size;
}
}
// If no single circuit can handle it, use partitioning
return this.planCircuitPartitioning(userTrustData);
}
}
Hierarchical Trust Computation
class HierarchicalEBSLProver {
/**
* Partition large trust networks into manageable subcircuits
*/
async generatePartitionedProof(
userAddress: string,
trustNetwork: LargeTrustNetwork
): Promise<AggregateProof> {
// 1. Partition network into communities/clusters
const communities = await this.partitionNetwork(trustNetwork);
// 2. Compute community-level reputation scores
const communityProofs: CommunityProof[] = [];
for (const community of communities) {
const communityReputation = await this.computeCommunityReputation(userAddress, community);
const communityProof = await this.generateCommunityProof(communityReputation, community.id);
communityProofs.push(communityProof);
}
// 3. Aggregate community proofs into final reputation
const aggregateProof = await this.aggregateCommunityProofs(communityProofs);
return aggregateProof;
}
private async partitionNetwork(network: LargeTrustNetwork): Promise<TrustCommunity[]> {
// Use community detection algorithms (Louvain, Label Propagation, etc.)
const communities: TrustCommunity[] = [];
// Implement community detection
const louvainResult = await this.louvainCommunityDetection(network);
for (const cluster of louvainResult.clusters) {
communities.push({
id: cluster.id,
nodes: cluster.nodes,
edges: cluster.internalEdges,
bridgeEdges: cluster.externalEdges,
cohesionScore: cluster.modularity,
});
}
return communities;
}
}Cross-Platform Reputation Fusion
interface ReputationSource {
platformId: string;
userIdentity: string;
reputationScore: number;
trustNetworkHash: string;
proofOfComputation: ZKProof;
weight: number;
validUntil: number;
}
class MultiSourceAggregator {
/**
* Aggregate reputation from multiple platforms/sources
*/
async aggregateMultiSourceReputation(
sources: ReputationSource[],
aggregationStrategy: AggregationStrategy
): Promise<AggregateReputationProof> {
// 1. Verify individual source proofs
const verifiedSources = await this.verifySourceProofs(sources);
// 2. Apply source weighting and normalization
const weightedScores = this.applySourceWeights(verifiedSources, aggregationStrategy);
// 3. Compute aggregate score using specified strategy
const aggregateScore = this.computeAggregateScore(weightedScores, aggregationStrategy);
// 4. Generate proof of correct aggregation
const aggregationProof = await this.generateAggregationProof(
verifiedSources,
weightedScores,
aggregateScore
);
return {
aggregateScore,
sourceCount: verifiedSources.length,
aggregationStrategy,
proof: aggregationProof,
validity: this.computeAggregateValidity(verifiedSources),
metadata: {
sources: verifiedSources.map((s) => ({
platformId: s.platformId,
weight: s.weight,
contribution: this.computeContribution(s, aggregateScore),
})),
timestamp: Date.now(),
algorithm: "MultiSourceEBSL-v1.0",
},
};
}
private computeAggregateScore(
weightedScores: WeightedScore[],
strategy: AggregationStrategy
): number {
switch (strategy.type) {
case "weighted_average":
return this.weightedAverage(weightedScores);
case "harmonic_mean":
return this.harmonicMean(weightedScores);
case "ebsl_fusion":
return this.ebslMultiSourceFusion(weightedScores);
case "max_entropy":
return this.maxEntropyAggregation(weightedScores);
default:
throw new Error(`Unknown aggregation strategy: ${strategy.type}`);
}
}
}Reputation Evolution Proofs
interface TemporalReputationSnapshot {
timestamp: number;
reputationScore: number;
networkState: string; // Merkle root
attestationCount: number;
computationProof: ZKProof;
}
class TemporalAggregator {
/**
* Generate proof of consistent reputation over time
*/
async generateTemporalConsistencyProof(
userAddress: string,
timeWindow: TimeWindow,
snapshots: TemporalReputationSnapshot[]
): Promise<TemporalConsistencyProof> {
// 1. Validate temporal snapshots
const validSnapshots = this.validateTemporalSequence(snapshots, timeWindow);
// 2. Compute temporal metrics
const temporalMetrics = this.computeTemporalMetrics(validSnapshots);
// 3. Generate proof of temporal properties
const consistencyProof = await this.generateConsistencyCircuitProof({
snapshots: validSnapshots,
metrics: temporalMetrics,
requirements: timeWindow.requirements,
});
return {
userAddress,
timeWindow,
snapshotCount: validSnapshots.length,
temporalMetrics,
consistencyProof,
verified: true,
};
}
private computeTemporalMetrics(snapshots: TemporalReputationSnapshot[]): TemporalMetrics {
const scores = snapshots.map((s) => s.reputationScore);
return {
averageScore: this.average(scores),
minimumScore: Math.min(...scores),
maximumScore: Math.max(...scores),
variance: this.variance(scores),
trendDirection: this.computeTrend(scores),
volatility: this.computeVolatility(scores),
consistencyIndex: this.computeConsistency(scores),
};
}
}Worker Pool Architecture
class EBSLWorkerPool {
private workers: Worker[] = [];
private taskQueue: Queue<EBSLTask> = new Queue();
private maxWorkers: number;
constructor(maxWorkers: number = 4) {
this.maxWorkers = maxWorkers;
this.initializeWorkers();
}
async processEBSLComputation(task: EBSLTask): Promise<EBSLResult> {
return new Promise((resolve, reject) => {
// Add task to queue with completion callback
this.taskQueue.enqueue({
...task,
onComplete: resolve,
onError: reject,
});
this.processNextTask();
});
}
private async processNextTask(): Promise<void> {
if (this.taskQueue.isEmpty() || this.allWorkersActive()) {
return;
}
const availableWorker = this.getAvailableWorker();
const nextTask = this.taskQueue.dequeue();
if (availableWorker && nextTask) {
await this.assignTaskToWorker(availableWorker, nextTask);
}
}
private async assignTaskToWorker(worker: Worker, task: EBSLTask): Promise<void> {
worker.postMessage({
type: "EBSL_COMPUTATION",
data: {
opinions: task.opinions,
networkData: task.networkData,
circuitType: task.circuitType,
},
});
worker.onmessage = (event) => {
if (event.data.type === "EBSL_RESULT") {
task.onComplete(event.data.result);
this.processNextTask(); // Process next queued task
} else if (event.data.type === "EBSL_ERROR") {
task.onError(new Error(event.data.error));
}
};
}
}Intelligent Circuit Caching
class EBSLCache {
private circuitCache: Map<string, CompiledCircuit> = new Map();
private proofCache: Map<string, CachedProof> = new Map();
private reputationCache: Map<string, CachedReputation> = new Map();
async getCachedProof(cacheKey: string): Promise<CachedProof | null> {
const cached = this.proofCache.get(cacheKey);
if (cached && !this.isExpired(cached)) {
return cached;
}
return null;
}
async cacheProof(cacheKey: string, proof: ZKProof, ttl: number): Promise<void> {
const cachedProof: CachedProof = {
proof,
timestamp: Date.now(),
expiresAt: Date.now() + ttl,
accessCount: 0,
};
this.proofCache.set(cacheKey, cachedProof);
// Implement LRU eviction if cache is full
this.enforceMaxCacheSize();
}
generateCacheKey(
userAddress: string,
networkStateHash: string,
circuitType: string,
proofType: string
): string {
return `${userAddress}:${networkStateHash}:${circuitType}:${proofType}`;
}
private isExpired(cached: CachedProof): boolean {
return Date.now() > cached.expiresAt;
}
}EBSL Mathematical Properties
import { property, assert } from "fast-check";
describe("EBSL Algorithm Properties", () => {
it("should satisfy commutativity", () => {
property(generateValidOpinion(), generateValidOpinion(), (op1, op2) => {
const result1 = ebslFusion(op1, op2);
const result2 = ebslFusion(op2, op1);
return approximatelyEqual(result1, result2, 1e-10);
});
});
it("should satisfy associativity", () => {
property(
generateValidOpinion(),
generateValidOpinion(),
generateValidOpinion(),
(op1, op2, op3) => {
const result1 = ebslFusion(ebslFusion(op1, op2), op3);
const result2 = ebslFusion(op1, ebslFusion(op2, op3));
return approximatelyEqual(result1, result2, 1e-10);
}
);
});
it("should preserve probability constraints", () => {
property(generateValidOpinion(), generateValidOpinion(), (op1, op2) => {
const result = ebslFusion(op1, op2);
return (
result.belief >= 0 &&
result.belief <= 1 &&
result.disbelief >= 0 &&
result.disbelief <= 1 &&
result.uncertainty >= 0 &&
result.uncertainty <= 1 &&
result.base_rate >= 0 &&
result.base_rate <= 1 &&
Math.abs(result.belief + result.disbelief + result.uncertainty - 1) < 1e-10
);
});
});
});Complete Proof Generation Pipeline
describe("Complete EBSL Proof Pipeline", () => {
it("should generate valid proof for realistic trust network", async () => {
// Given: A realistic trust network
const userAddress = "0x1234567890123456789012345678901234567890";
const trustNetwork = await generateRealisticTrustNetwork(userAddress, {
attestationCount: 25,
networkSize: 500,
averageConnectivity: 3.5,
});
// When: We generate a reputation proof
const proofResult = await ebslProver.generateProof({
userAddress,
trustNetwork,
proofType: "public_threshold",
threshold: 0.6,
});
// Then: The proof should be valid and verifiable
expect(proofResult.success).toBe(true);
expect(proofResult.proof).toBeDefined();
expect(proofResult.reputationScore).toBeGreaterThanOrEqual(0.6);
// And: The proof should verify on-chain
const verificationResult = await verifyProofOnChain(proofResult.proof);
expect(verificationResult.isValid).toBe(true);
});
});This comprehensive EBSL integration strategy provides the foundation for implementing scalable, secure, and efficient zero-knowledge reputation proofs that can handle arbitrary network sizes while maintaining mathematical rigor and performance requirements.