Web3 Privacy Hub
Explore how privacy evolved, how Inco transforms on-chain confidentiality, and why this unlocks mass adoption.
Evolution of Privacy in Web3
Early Privacy: Mixers, CoinJoin, and Stealth Addresses.
When Bitcoin appeared in 2009, it was often called an “anonymous currency.”
However, as the ecosystem developed, it became clear that Bitcoin is not truly anonymous, but pseudonymous - all transactions are permanently visible on the blockchain, and anyone can trace the flow of funds from one address to another.
This realization sparked the emergence of early privacy solutions, which aimed to partially obscure transactions long before zk-protocols, FHE, and TEE computations were available.
Early solutions included:
Cryptocurrency Mixers
CoinJoin
Stealth addresses
1. Mixers: The First Attempt to Hide Traces.
A mixer is a service that accepts users’ coins, “shuffles” them with other participants’ coins or its own liquidity pools, and then sends back new coins that are not directly linked to the original transactions.
The main idea:
Make it impossible to determine which inputs correspond to which outputs.
A user sends funds to the service.
The mixer aggregates deposits from multiple users.
After some time, it sends back equivalent amounts to each participant, but from completely different addresses.
Problems with mixers:
Centralization → the service could steal funds.
Log leaks → some mixers compromised user privacy.
Legal risks → governments started treating mixers as money laundering services.
Mixers were an important step for privacy but had serious weaknesses, leading the industry to develop cryptographically grounded solutions.
2. CoinJoin: Decentralized Mixing
CoinJoin is a method where multiple users combine their transactions into one, making it impossible to determine which input belongs to which output.
Each transaction can be easily linked to its sender.
With CoinJoin:
A single large transaction is created that combines all inputs and outputs.
Inputs:
• 1 BTC from A
• 1 BTC from B
• 1 BTC from C
Outputs:
• 1 BTC to X
• 1 BTC to Y
• 1 BTC to Z
All outputs are equal, and it is impossible to determine which input corresponds to which output.
Although CoinJoin has advantages - decentralization and no need for trust, there are critical disadvantages: vulnerability to graph analysis, Sybil attacks, and legal and regulatory issues.
3. Stealth Addresses: Hiding the Recipient
If you know someone’s public address, you can see all incoming and outgoing transactions for that address forever.
Stealth Addresses solve this problem
The sender generates a unique one-time address for each transaction.
The recipient uses their private key to scan the blockchain and determine which addresses belong to them.
Result: no one else can link transactions to the recipient's real public address.
Stealth addresses became an important historical step in the development of privacy (For example, with Monero), but at that time the technology had disadvantages:
Increased network load
A more complex user interface for users
Some early implementations had weaknesses in cryptography
Although the methods described above often only provide an illusion of privacy, as they are more of an attempt to hide data within the system, they nevertheless represent an important step in the development of privacy-enhancing technologies and have led to the emergence of:
Privacy-focused cryptocurrencies
zk-protocols
Private computations (TEE, MPC, FHE)
As cryptocurrencies develop, it is becoming increasingly clear that:
Users need privacy
Open blockchain data can be dangerous
Privacy is not a matter of “criminality” but of security, freedom, and data protection.
Privacy is essential for mass adoption.
All of this has contributed to the emergence of solutions such as
Inco network - a modern layer of confidentiality for Web3.
In the following sections, we will continue to discuss the evolution of privacy and take a closer look at more modern solutions.
When Bitcoin appeared in 2009, it was often called an “anonymous currency.”
However, as the ecosystem developed, it became clear that Bitcoin is not truly anonymous, but pseudonymous - all transactions are permanently visible on the blockchain, and anyone can trace the flow of funds from one address to another.
This realization sparked the emergence of early privacy solutions, which aimed to partially obscure transactions long before zk-protocols, FHE, and TEE computations were available.
Early solutions included:
Cryptocurrency Mixers
CoinJoin
Stealth addresses
1. Mixers: The First Attempt to Hide Traces.
A mixer is a service that accepts users’ coins, “shuffles” them with other participants’ coins or its own liquidity pools, and then sends back new coins that are not directly linked to the original transactions.
The main idea:
Make it impossible to determine which inputs correspond to which outputs.
A user sends funds to the service.
The mixer aggregates deposits from multiple users.
After some time, it sends back equivalent amounts to each participant, but from completely different addresses.
Problems with mixers:
Centralization → the service could steal funds.
Log leaks → some mixers compromised user privacy.
Legal risks → governments started treating mixers as money laundering services.
Mixers were an important step for privacy but had serious weaknesses, leading the industry to develop cryptographically grounded solutions.
2. CoinJoin: Decentralized Mixing
CoinJoin is a method where multiple users combine their transactions into one, making it impossible to determine which input belongs to which output.
Each transaction can be easily linked to its sender.
With CoinJoin:
A single large transaction is created that combines all inputs and outputs.
Inputs:
• 1 BTC from A
• 1 BTC from B
• 1 BTC from C
Outputs:
• 1 BTC to X
• 1 BTC to Y
• 1 BTC to Z
All outputs are equal, and it is impossible to determine which input corresponds to which output.
Although CoinJoin has advantages - decentralization and no need for trust, there are critical disadvantages: vulnerability to graph analysis, Sybil attacks, and legal and regulatory issues.
3. Stealth Addresses: Hiding the Recipient
If you know someone’s public address, you can see all incoming and outgoing transactions for that address forever.
Stealth Addresses solve this problem
The sender generates a unique one-time address for each transaction.
The recipient uses their private key to scan the blockchain and determine which addresses belong to them.
Result: no one else can link transactions to the recipient's real public address.
Stealth addresses became an important historical step in the development of privacy (For example, with Monero), but at that time the technology had disadvantages:
Increased network load
A more complex user interface for users
Some early implementations had weaknesses in cryptography
Although the methods described above often only provide an illusion of privacy, as they are more of an attempt to hide data within the system, they nevertheless represent an important step in the development of privacy-enhancing technologies and have led to the emergence of:
Privacy-focused cryptocurrencies
zk-protocols
Private computations (TEE, MPC, FHE)
As cryptocurrencies develop, it is becoming increasingly clear that:
Users need privacy
Open blockchain data can be dangerous
Privacy is not a matter of “criminality” but of security, freedom, and data protection.
Privacy is essential for mass adoption.
All of this has contributed to the emergence of solutions such as
Inco network - a modern layer of confidentiality for Web3.
In the following sections, we will continue to discuss the evolution of privacy and take a closer look at more modern solutions.
As Bitcoin and Ethereum grew in popularity, it became clear that financial privacy had practically disappeared, and network congestion was increasing. Early solutions aimed at achieving privacy - or rather, masking transactions - showed that more precise and secure mechanisms were needed. This led to the adoption of Zero-Knowledge Proof (ZKP) techniques.
The first practical implementation of ZK was demonstrated in Zcash: a user creates a transaction and generates a zk-SNARK that verifies its correctness.
For the first time, it became possible to:
- hide the sender
- hide the receiver
- hide the amount
- while proving that the transaction is correct and mathematically verified
After successful implementation, ZK technology began to develop rapidly in other areas of Web3. It stopped being just a tool for private coins and started being used for blockchain scaling, protecting user data, and ensuring trust in off-chain computations.
Zk-rollups appeared - layer-2 solutions for Ethereum that allow hundreds or thousands of transactions to be processed off-chain while only sending a compact proof of correctness to the blockchain. This reduces network load, speeds up transaction confirmation, and lowers fees.
Projects using zk-ID are also actively developing, allowing users to prove their age, community membership, or voting rights without revealing personal data.
Overall, ZK has become one of the pillars of private Web3.
The principle of ZK can be explained simply: a user wants to prove they possess certain information or assets without revealing the information itself.
In blockchain networks, this works as follows:
- the user creates a transaction (or other action)
- a mathematical proof is generated to confirm the correctness of the operation
- a network node verifies the proof without seeing the underlying data
The process involves two roles:
• Prover - creates the proof
• Verifier - checks its correctness
As a result, zero-knowledge proofs can be applied to a wide range of tasks while maintaining trust and confidentiality.
Over time, Zero-Knowledge has significantly evolved. Today, there are many types of ZK proofs that solve different tasks.
For example, zk-STARKs do not require a trusted setup and simplify the generation of proofs compared to classic SNARKs.
ZK proofs can also be:
- interactive, where parties exchange messages
- non-interactive, where correctness is confirmed with a single proof
Zero-Knowledge provides several key benefits to Web3:
- Privacy. User data and transaction details are hidden but remain verifiable by the network.
- Scalability. zk-rollups allow computations to be moved off the main chain, sending only final proofs to the blockchain, reducing load and fees.
- Decentralized identity. Users can verify personal details, such as age or membership, without sharing sensitive information
- Mathematical verification. Actions are verified cryptographically, without trusting intermediaries.
Despite its advantages, ZK has certain drawbacks:
- generating proofs requires significant computational resources
- developing ZK-based solutions is complex and requires deep cryptography knowledge
- SNARKs require a trusted setup, which poses security risks if compromised
- Regulatory barriers may limit the use of ZK and create potential legal challenges
Zero-Knowledge has shown that privacy and verifiability in Web3 are possible, but it remains just one approach and does not solve all challenges. The Web3 ecosystem also uses other methods - TEE, FHE, and MPC, to protect data and ensure confidential computing in various scenarios. This emphasizes that building a secure and confidential Web3 requires a combination of several approache
When early methods of data protection could no longer cope with the complexity of modern systems, and cryptographic schemes like ZK only addressed part of the tasks, a new requirement emerged: not just to hide data, but to perform computations on it without revealing it
This became possible thanks to private computation technologies: TEE, MPC, and FHE. Each technology ensures confidentiality in its own way and addresses different levels of tasks.
TEE (Trusted Execution Environment) is an isolated, secure area within a processor where computations can be performed in such a way that neither the operating system, nor the device administrator, nor any third-party software has access to the data or the code being executed.
TEE provides:
FHE (Fully Homomorphic Encryption) is a type of encryption that allows performing operations on data without decrypting it.
The most important property: the result of computations after decryption appears as if the computations were performed on plaintext data.
In other words, data remains encrypted throughout - during storage, transmission, and even during processing. The service performing the computation never sees the original data but can still correctly process it.
This enables fully untrusted infrastructure: servers can process encrypted data without knowing anything about it.
However, FHE has limitations:
MPC (Secure Multi-Party Computation) is a cryptographic method in which multiple participants jointly compute a function on their data without revealing it to each other.
MPC provides:
Unlike TEE, MPC does not require trust in a single server - security is achieved through cryptography and distribution of data among participants. Compared to FHE, MPC is often more efficient for distributed scenarios but requires good synchronization between participants and additional communication resources.
Due to high communication and computational overhead, complexity of protocols and integration, as well as sensitivity to the number of participants, MPC can be slower and more difficult to deploy compared to local solutions like TEE.
However, to improve efficiency and security, MPC can be combined with FHE or TEE, which allows either performing part of the computation on encrypted data (FHE) or accelerating distributed computations using secure hardware enclaves (TEE), reducing communication overhead and increasing protocol performance.
Thus, private computation is a key tool for modern systems, where it is important not only to store data confidentially but also to safely perform computations on it. In real-world applications, combinations of these technologies are often used to achieve an optimal balance between security, performance, and scalability.
The choice of a specific approach depends on privacy requirements, trust in the infrastructure, and computational resources, making private computation a flexible and promising tool for future digital systems.
This became possible thanks to private computation technologies: TEE, MPC, and FHE. Each technology ensures confidentiality in its own way and addresses different levels of tasks.
TEE (Trusted Execution Environment) is an isolated, secure area within a processor where computations can be performed in such a way that neither the operating system, nor the device administrator, nor any third-party software has access to the data or the code being executed.
TEE provides:
- isolated execution environment;
- hardware-based integrity verification;
- data secrecy during computation.
FHE (Fully Homomorphic Encryption) is a type of encryption that allows performing operations on data without decrypting it.
The most important property: the result of computations after decryption appears as if the computations were performed on plaintext data.
In other words, data remains encrypted throughout - during storage, transmission, and even during processing. The service performing the computation never sees the original data but can still correctly process it.
This enables fully untrusted infrastructure: servers can process encrypted data without knowing anything about it.
However, FHE has limitations:
- computations are orders of magnitude slower than ordinary ones;
- high resource requirements;
- complexity of implementation and limited set of operations (although this is gradually expanding).
MPC (Secure Multi-Party Computation) is a cryptographic method in which multiple participants jointly compute a function on their data without revealing it to each other.
MPC provides:
- distributed computation;
- confidentiality of each participant's input data;
- correctness of the result without revealing intermediate information.
Unlike TEE, MPC does not require trust in a single server - security is achieved through cryptography and distribution of data among participants. Compared to FHE, MPC is often more efficient for distributed scenarios but requires good synchronization between participants and additional communication resources.
Due to high communication and computational overhead, complexity of protocols and integration, as well as sensitivity to the number of participants, MPC can be slower and more difficult to deploy compared to local solutions like TEE.
However, to improve efficiency and security, MPC can be combined with FHE or TEE, which allows either performing part of the computation on encrypted data (FHE) or accelerating distributed computations using secure hardware enclaves (TEE), reducing communication overhead and increasing protocol performance.
Thus, private computation is a key tool for modern systems, where it is important not only to store data confidentially but also to safely perform computations on it. In real-world applications, combinations of these technologies are often used to achieve an optimal balance between security, performance, and scalability.
The choice of a specific approach depends on privacy requirements, trust in the infrastructure, and computational resources, making private computation a flexible and promising tool for future digital systems.
Inco Architecture and Technologies
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Privacy is the foundation of free societies..
The impact of Inco on mass adoption of Web3
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