Wasabi 2.x Coinjoin: Advanced Bitcoin Privacy

• Wasabi 2.x Coinjoin is a Bitcoin privacy protocol that employs coordinator-assisted mixes with standardized output denominations to obfuscate transaction linkages.
• The protocol uses structured rounds, Bayesian mapping, and fixed heuristics to significantly reduce input-output correlation and maintain anonymity set loss below 20%.
• Empirical studies and advanced forensic analyses validate Wasabi 2.x’s enhanced privacy compared to predecessors such as Wasabi 1.x and Whirlpool, while leveraging Tor for secure communications.

Wasabi 2.x Coinjoin is a collaborative Bitcoin transaction protocol designed to maximize privacy by obfuscating the linkage between transaction inputs and outputs through structured, coordinator-assisted mixes with standardized output denominations. The Wasabi 2.x architecture features substantial improvements in privacy engineering, detection resistance, and post-mix protection relative to earlier Coinjoin designs.

1. Protocol Overview and Structure

Wasabi 2.x Coinjoin operates as an interactive privacy protocol wherein multiple users combine their UTXOs (inputs) into one transaction with multiple outputs, managed by a central coordinator but leveraging cryptographic primitives to limit linkage. The system uses structured rounds in which participants are assigned outputs from a fixed, standardized set of denominations $\mathcal{D}$, and transaction construction is governed by the WabiSabi protocol. This coordination allows for a large anonymity set while employing mechanisms to prevent input–output correlation.

Key features include:

• Multiple inputs per participant, with $a_{max}$ limiting the number per user.
• Multiple outputs per participant selectable from $\mathcal{D}$, potentially used more than once.
• Use of Tor for all communication, minimizing network-level metadata leakage.
• Uniform value outputs and optimization algorithms for output decomposition.

The system breaks common Bitcoin clustering heuristics by ensuring that outputs are uniformly standardized and by introducing flexible decomposition constraints in transaction assembly.

2. Detection Heuristics and Analytical Frameworks

Identifying Wasabi 2.x Coinjoins in the Bitcoin blockchain requires advanced heuristics described in (Schnoering et al., 2023). Detection centers on the following mathematical criteria:

• At least half the outputs (minus coordinator fee) must belong to standardized denominations:

$$|\{v \in \Delta_{out} \mid v \in \mathcal{D}\}| \geq \frac{|\Delta_{out}|-1}{2}$$

• The lower bound on input count dictated by protocol parameters $p$:

$|\Delta_{in}| \geq p$

• Inputs per participant restricted by $a_{max}$:

$n \geq \frac{|\Delta_{in}|}{a_{max}}$

• Minimum value for input coins:

$\min_{(p,v)\in\Delta_{in}} v \geq v_{min}$

• All output scripts are distinct:

$$|\Delta_{out}| = \text{number of distinct output scripts}$$

This detection strategy exploits Wasabi 2.x’s fixed denominations and output structure, making algorithmic identification precise unless adversarial protocols mimic these features.

3. Privacy Analytics and Anonymity Set Evolution

Privacy in Wasabi 2.x is quantitatively measured by the anonymity set $A(o)$ for each output $o$ defined as:

$A(o) = \{o' \in O \mid v(o') = v(o)\}$

where $O$ is the set of outputs and $v(\cdot)$ returns the output value. The post-mix anonymity set loss after $d$ days is evaluated as

$$A_d(o) = \frac{|\{o' \in A(o) \text{ consolidated within } d \text{ days}\}|}{|A(o)|}$$

A consolidation event is defined by the re-use of two or more outputs from the same coinjoin as inputs in a subsequent transaction, which can link ownership.

Empirical results show the average anonymity set loss in Wasabi 2.x remains below 20%, even accounting for undesirable post-mix behavior. Loss is greatest in the first day post-mix and stabilizes after one year, a marked improvement over earlier protocols such as Whirlpool (30–50% loss) and Wasabi 1.x (25–40%) (Gavenda et al., 20 Oct 2025).

4. Privacy Estimation and Mapping Enumeration

Privacy estimation adopts an exhaustive mapping enumeration strategy that models the Coinjoin as a set of subset-sum constraints:

$$\sum \text{inputs}_{u} = \sum \text{outputs}_{u} + \text{fees}$$

for each user $u$. The algorithm accounts for both deterministic and stochastic fees (decomposition and mining), incorporates Wasabi client-enforced input/output limits, and introduces tolerance $\delta$ for non-deterministic decomposition fees.

Mappings are evaluated using a Bayesian framework, where a mapping $M$ receives probability $p(M) = p(M|A)\cdot p(A)$ based on the randomness of output selection and observed user choices. A risk metric, $p(I, o) = \max_{i \in I} p(i, o)$, estimates the probability of linking any input $i$ of user $I$ to output $o$.

Simulation on real Wasabi 2.x coinjoins demonstrates that the space of valid mappings grows super-exponentially with coinjoin size, making exact attribution computationally infeasible in practice even for large mixes. Emulated and real-world transactions confirm the completeness of the algorithm and the robustness of privacy protection against mapping attacks.

5. Comparative Adoption Metrics and Privacy Evaluation

Measurement studies (Stütz et al., 2021) identified exactly 30,251 Wasabi CoinJoin transactions mixing 205,030.21 BTC (≈4.02 B USD) between July 2018–February 2022. Monthly mixing averages for Wasabi are 3,527.44 BTC (≈172.93 M USD). Compared with Samourai’s Whirlpool, which has more transactions but lower mixed values, Wasabi’s larger volumes result in greater attention from regulatory entities.

Empirical analysis shows the effective anonymity set is diminished by pre- and post-mix clusterings (“star pattern,” “collector pattern”), where users source inputs from a common wallet or collect outputs back to a single wallet, reducing unlinkability. Nonetheless, Wasabi 2.x’s protocol design minimizes such losses, especially by its flexible remix and output decomposition policies.

6. Practical Security, Forensic Considerations, and Recommendations

Forensic analysis (Young et al., 2021) reveals that while the on-chain Coinjoin design achieves output-indistinguishability ($\forall i, j: v_i = v_j$), in practice, privacy can be threatened by local artifacts (debug logs, wallet files) and network metadata. Experiments using Autopsy, FTK Imager, and AXIOM found minimal direct evidence linking Coinjoin inputs to outputs in Wasabi 2.x images, mainly due to wallet encryption (BIP38) and Tor network usage.

Key recommendations to prevent privacy loss include:

• Encrypt wallet and log files at rest, minimize debug logging.
• Regularly audit for accidental plaintext leakage.
• Secure and obfuscate coordinator operations; consider threshold signatures or multi-party computation for future improvements.
• Perform continuous forensic analysis to ensure privacy remains intact after wallet or dependency upgrades.

7. Systemic Implications, Accountability Mechanisms, and Future Research

Overlay deanonymization protocols (Keller et al., 2020) propose selective reduction of anonymity for law enforcement, using cryptographic commitments and signatures that allow witnesses to prove non-involvement with suspect outputs. Wasabi 2.x could integrate such protocols to balance privacy and legal accountability.

Ongoing research avenues include refining mapping enumeration algorithms for very large mixes, modeling fee structures with greater accuracy, and incentivizing wallet behaviors that minimize post-mix consolidation. As regulatory scrutiny increases and attacker techniques evolve (e.g., combining off-chain data), further developments in input/output rules and forensic resistance are anticipated.

Summary Table: Privacy Loss Across Coinjoin Designs

Protocol	Anonymity Set Loss (Avg)	Time Profile
Whirlpool	30–50%	Highest in first day
Wasabi 1.x	25–40%	Highest in first day
Wasabi 2.x	<20%	Highest in first day, negligible after 1 yr

In conclusion, Wasabi 2.x Coinjoin incorporates major advances in coordinator-driven transaction privacy for Bitcoin, with robust on-chain and forensic defenses, low practical anonymity set loss, and systematic privacy estimation techniques. Detection and analysis tools have evolved to accommodate its structural innovations, and research into the balance between privacy, accountability, and usability continues.