Comparison with Traditional DEXs
Comparison with Traditional DEXs
SnarkSide is not an optimization of existing DEX mechanics; it is a fundamental departure. While most decentralized exchanges — both spot and perpetual — rely on visibility to enforce coordination and settlement, SnarkSide treats visibility as an adversarial property, not a feature.
This section outlines the core distinctions between SnarkSide’s encrypted, intent-based architecture and traditional DEX models. The focus is on three key dimensions: the execution model (Orderbook vs Intent), the surface area exposed to adversaries in perpetuals, and the long-term structural degradation caused by full transparency.
Orderbook vs Intent
Traditional DEXs — such as Uniswap (v2/v3), dYdX (v3), GMX, and most off-chain orderbook systems — operate around one of two execution paradigms:
AMM-based price curves (constant product, stableswap, vAMM)
CLOB-based execution (central limit order books)
Both assume that visibility of liquidity and intent is necessary for coordination.
Order Visibility
Public
Encrypted
Slippage Exposure
Immediate
Hidden
Position Size
Observable
Committed, not revealed
Price Discovery
Reactive
Constraint intersection
Trade Execution
User-submitted transaction
Off-chain match + ZK settlement
Liquidity Discovery
Via orderbook depth or LP analysis
Private, batched, non-queryable
The most consequential difference is architectural: SnarkSide does not surface liquidity, does not expose resting orders, and does not resolve execution via on-chain triggers. Every trade is a resolution of compatible constraints, not a response to market stimuli.
In traditional systems, a user states: “buy 1 ETH at $3500 or better.” In SnarkSide, a user says: “prove that I could have bought 1 ETH under my slippage rules from a counterparty who agreed to sell.”
The market no longer exists as a visible arena. It exists as a proof boundary.
Perp DEX Attack Surfaces
Perpetual futures DEXs are particularly susceptible to visibility-based attacks, far more than spot exchanges. This is due to:
Leverage: Amplifies sensitivity to price movement.
Liquidation thresholds: Exposed publicly, creating known price targets.
Funding rate dependencies: Skew is calculated from visible OI imbalance.
Latency-sensitive traders: MEV searchers and liquidators can execute faster.
These attributes create several systemic threats in traditional perp protocols:
1. Liquidation Hunting
Since liquidation thresholds are derived from public position data, adversaries can identify vulnerable positions and manipulate price feeds to trigger them. This has historically resulted in high-leverage traders being wiped out due to minimal adverse price movement that would otherwise be tolerable in a private setting.
2. Oracle Manipulation
Where the oracle feed is thin or based on public orderbook aggregation, a sophisticated actor can nudge the reference price just far enough to:
Trigger funding imbalance
Push liquidations
Invalidate opposing positions
All of these actions are incentivized when the position structure is observable.
3. Frontrunning and MEV
Any on-chain execution path involving a mempool transaction is exposed to MEV bots. In high volatility markets, a user broadcasting a large market order can have their position sandwiched, slipped, or replicated and reversed by a faster actor. This is especially punishing in leverage-enabled environments.
4. Orderflow Simulation
By continuously monitoring order placement, cancellation, and liquidity migration, adversaries can train statistical models that anticipate price direction. In a system where everyone sees everything, edge comes from simulation, not participation.
SnarkSide was designed to remove all four of these attack surfaces.
Why Transparency Fails at Scale
The idealism of transparency as a public good breaks down in adversarial financial environments. What begins as openness rapidly transforms into real-time telemetry for attack optimization.
At scale, transparent perp DEXs experience structural failures:
Feedback Loops
Visible open interest creates reflexive funding pressures. Traders can analyze skew and position accordingly, forcing rebalances that never resolve.
Liquidity Drain
LPs suffer as their positions are parsed, mirrored, or front-run. Over time, only the most latency-optimized or fee-insensitive LPs remain, degrading liquidity quality.
Execution Inequality
Traders with access to faster RPC nodes, private relays, or custom bundlers outperform everyone else. This violates DeFi’s foundational principle of equal access.
Trader Self-Censorship
Once participants realize that execution leaks alpha, they reduce size, delay entry, or avoid on-chain activity altogether — diminishing the system’s utility.
SnarkSide circumvents these failure modes by eliminating the conditions that make them possible. Its encrypted architecture allows:
Orderflow without observable intent
Position entry without liquidation anchoring
Funding curves without public skew dependence
Execution without mempool-based reordering
In SnarkSide, truth is enforced cryptographically, not by observation.
Visibility is optional. Validity is not.
Last updated