How Zero-Knowledge Proofs Are Changing the Privacy and Security

There has been a growing concern over data privacy and security in the digital age. As more personal and sensitive information is transmitted and stored online, the risk of data breaches and unauthorized access becomes an ever-present threat. This is particularly true in blockchain technology, where the transparency and immutability of transactions on a public ledger can sometimes come at the cost of privacy. However, a solution to this problem has emerged in the form of Zero-Knowledge Proofs (ZKPs), a type of cryptographic protocol that allows verifying transactions and statements without revealing the underlying data.

This article seeks to answer these questions and delve deeper into the fascinating world of Zero-Knowledge Proofs. We will explore the conceptual foundations of ZKPs, compare and contrast different types of ZKP systems, examine their diverse applications across industries, discuss their implications for data privacy and security, and speculate on their future trajectory. Join us on this journey as we uncover the intricacies of ZKPs and discover how they might shape our digital future.

What are Zero-Knowledge Proofs (ZKPs)

Zero-knowledge proofs (ZKPs) are a cryptographic tool that allows one party (the prover) to prove a statement’s truth to another party (the verifier) without revealing any additional information apart from the statement’s truth. This method is based on proving the possession of specific knowledge without disclosing it. ZKPs have become crucial to privacy-preserving cryptographic applications, particularly when disclosing underlying data or computation details is undesirable or risky. They are particularly important in blockchain technology, allowing for secure, private transactions while maintaining transparency.

Cryptographic techniques called Zero-Knowledge Proofs (ZKPs) allow a prover to convince a verifier that a statement is true without sharing further information. ZKPs introduce new ways of representing, manipulating, and reasoning about information, creating a new type of computing. ZKPs represent significant progress in cryptography and privacy-preserving computation. They allow for more expressive and powerful privacy-preserving protocols because they emphasize the interactions and transformations between different bits of information rather than the specific values themselves.

ZKPs provide a unique computing paradigm that emphasizes computation and protects privacy. This enables the validation of computations without disclosing the underlying information or the computation’s details. Zero-knowledge proofs are a type of knowledge-compression technology since they enable the prover to persuade the verifier of a statement’s truth without transmitting the full witness. As a result, zero-knowledge proofs have a smaller proof size and require less communication overhead, making them suitable for applications where privacy is critical or there are bandwidth constraints.

Here’s an example of a zero-knowledge proof, often referred to as the “Gold Coin” problem:

Imagine that you have a friend who has a gold coin. You want to prove to someone else that your friend has the coin without showing it. You can do this by demonstrating your friend’s possession of the coin without revealing any information.

Here’s how it works

1. Your friend puts the gold coin in a box and gives it to you.
2. You take the box and put it in a larger box, which you also give to your friend.
3. Your friend adds some weight to the larger box, enough to balance out the weight of the smaller box containing the gold coin.
4. You ask your friend to close the larger box and seal it.
5. You take the larger box and return it to the person you want to convince.
6. You challenge the person to guess which box contains the gold coin. They can choose either the small box or the large box.
7. Once they’ve made their choice, you open the box they didn’t choose and show them that it’s empty.
8. Since the weights are balanced, the remaining box must contain the gold coin.

This process allows you to prove that your friend has the gold coin without revealing anything about it. Even though the person you’re trying to convince doesn’t get to see inside the boxes, they can still verify that the boxes are properly sealed and that the weights are balanced, ensuring that the outcome is fair.

This demonstration uses a combination of secrecy and cleverness to allow one party (you) to prove to another party (your friend) that a statement (your friend has the gold coin) is true without revealing any additional information beyond the validity of the statement itself.

The significance of ZKPs in cryptography is similar — they allow for the verification of a claim (like a password, a transaction detail, or a data point) without exposing any additional information about the claim itself.

Why are zero-knowledge proofs necessary?

Zero-knowledge proofs are incredibly valuable as they open doors to various innovative applications that wouldn’t be feasible otherwise. Here are some examples:

1. Private blockchain transactions: Cryptocurrencies such as Zcash use zero-knowledge proofs to keep details about the sender, receiver, and transaction hidden while allowing the network to confirm transactions. This ensures strong privacy for users.
2. Decentralized identity: With zero-knowledge proofs, you can verify your identity online without disclosing sensitive information like your social security number, maintaining your privacy.
3. Secure multi-party computation: Zero-knowledge proofs enable multiple parties to collaborate on computing tasks using their private data without exposing it to others. This allows for confidential data analysis while preserving privacy.
4. Trustworthy electronic voting: Zero-knowledge proofs can be used in voting systems to ensure that votes are counted accurately without revealing individual voting choices. This prevents any attempts at buying or selling votes.
5. Verifiable outsourced computation: Using zero-knowledge proofs, blockchains can delegate certain computations off-chain and accept the results only if they come with proof of validity. This helps in maintaining security while enhancing scalability.

Zero-knowledge proofs are an effective tool that can be used to provide safe identity verification, improve scalability and interoperability, comply with regulations, and improve privacy and security. They make information verifiable without disclosing the underlying data. This offers high privacy and security, crucial in areas like personal identification and finance.

ZKPs are especially useful in blockchain technology because they enable transaction verification without disclosing the underlying data, enhancing the privacy and scalability of blockchain networks.

Fundamentals of Zero-Knowledge Proofs

In the context of Zero-Knowledge Proofs (ZKPs), the concept of ‘proof’ is distinct and technically profound. It involves a communication process between two parties: a prover and a verifier. The prover must convince the verifier of the truth of a statement without revealing any information beyond the validity of the statement itself.

Three fundamental properties define a zero-knowledge proof:

1. Completeness: If the statement is true, an honest prover can convince an honest verifier.
2. Soundness: If the statement is false, no cheating prover can convince an honest verifier that it is true, except with some small probability.
3. Zero-knowledge: If the statement is true, the verifier learns nothing other than the fact that the statement is true.

Technical Aspects:

• Witness: In ZKPs, a witness is a particular piece of knowledge or secret the prover has and tries to verify with the verifier. In essence, the concealed information supports the proposition’s veracity. The witness is never disclosed to the verifier directly in a zero-knowledge scenario. Rather, the proof establishes its validity or existence, with the prover proving their familiarity with the witness without disclosing it. The idea of the witness is essential because it supports the proof’s zero-knowledge component, which ensures the verifier only gains knowledge about the witness’s validity and nothing more.
• Provers: In ZKPs, the parties tasked with proving a specific assertion or claim to the verifier are known as provers. Their job is to prove, without naming the witness directly, that they are aware of a particular witness (a secret or piece of information). The prover creates a proof to persuade the verifier that the statement is true. To comply with the zero-knowledge properties, provers must produce a proof that verifies the truth (completeness), prevents the acceptance of false claims (soundness), and divulges no information other than the fact that the statement is true (zero-knowledge). ZKPs are an effective tool in cryptography because the prover can persuade the verifier without disclosing the witness or providing any extra information.
• Verifiers: A verifier is a party that receives the proof from the prover and checks its validity. If the proof is valid, the verifier learns that the witness’s data has the claimed properties but still knows nothing about the actual values of the data. The verifier can then provide a validation certificate to the witness, confirming that the data exists and has the claimed properties without learning anything about it.
• Use of Randomness: Both the prover and verifier can utilize randomness in their process. This randomness is critical in ensuring the unpredictability and security of the proof.
• Types of Statements: ZKPs are particularly relevant for complex computational problems where the verifier cannot determine the answer in polynomial time. Examples include NP-complete problems or calculations like the discrete logarithm of a given value in a group.
• Witness, Challenge, and Response: The prover starts by choosing a question (witness), calculating the answer, and sending it to the verifier. The verifier then asks the prover to answer another question (challenge). The prover responds (response), and this interaction is repeated until the verifier is satisfied. This process ensures that the verifier can verify the truth of a claim without learning any information about the claim itself.

In practical terms, a ZKP allows proving the truth of a statement without sharing the statement’s contents or revealing how the truth was discovered. This is achieved through algorithms that take data as input and return ‘true’ or ‘false’ as output, maintaining the privacy and confidentiality of the underlying data or statement.

Types of ZKP: Interactive and Non-Interactive Proofs

Interactive Zero Knowledge Proof (iZKP)

An Interactive Zero Knowledge Proof (iZKP) is a method that requires interaction between the prover and the verifier to ensure that the proof of knowledge is carried out correctly. In an iZKP, both parties engage in a series of rounds, exchanging messages to prove that the prover knows the question. One of the most commonly used iZKPs is the Fiat-Shamir heuristic. In this method, a random value is generated and used as a challenge to the prover. The prover must then use their secret knowledge to produce a response to the challenge, which the verifier can verify as correct. If the response is correct, the verifier accepts the proof and completes the interaction.

One of the key advantages of iZKPs is that they are highly flexible and can be used to prove knowledge of many different types of information. For example, they can be used to prove that someone knows a secret value, such as a password, or that they have a certain authority over a digital asset, such as a cryptocurrency. Another advantage of iZKPs is that they are very secure. Random challenges and the requirement for interaction between the prover and the verifier make it extremely difficult for an attacker to compromise the proof.

However, iZKPs also have some disadvantages. One of the main drawbacks is that they require a high level of trust between the prover and the verifier. If either party is compromised, the security of the proof can be compromised. Additionally, iZKPs can be time-consuming and require significant computational resources, making them impractical for some applications.

Non-Interactive Zero Knowledge Proof (niZKP)

Non-Interactive Zero Knowledge Proofs, on the other hand, are ZKPs that do not require interaction between the prover and the verifier. Instead, the prover creates a proof that the verifier can verify without interaction.

One of the most common niZKPs is the SNARK, which stands for Succinct Non-Interactive Argument of Knowledge. SNARKs use advanced mathematical algorithms to create a proof that the verifier can verify without interaction. The proof is typically a short, highly compressed representation of the information that the prover wants to prove, and the verifier can use this representation to confirm that the proof is valid.

The main advantage of niZKPs is that they are much more efficient and scalable than iZKPs. Because there is no interaction between the prover and the verifier, niZKPs can be verified much faster and with fewer computational resources. Additionally, because they do not require interaction, niZKPs are well suited for use in decentralized systems, such as blockchain networks, where trust between parties is difficult to establish.

Like iZKPs, niZKPs also have their own disadvantages. One of the main drawbacks is that they are less flexible than iZKPs, and can only be used to prove knowledge of certain types of information. Additionally, niZKPs can be more difficult to implement and require higher technical expertise.

Transforming Interactive Proofs into Non-Interactive Proofs

The Fiat-Shamir heuristic is a fundamental transformation in cryptographic proofs, particularly in the context of zero-knowledge proofs. It serves as a bridge between interactive and non-interactive proof systems. To understand this in more detail, let’s break down the process and its implications:

1. Interactive Proof Systems:

• In interactive proof systems, the prover and verifier engage in a series of exchanges. The verifier challenges the prover with specific queries or tasks, and the prover responds with answers that aim to prove the validity of their statement.
• This interaction is crucial for the verifier to gain confidence in the truth of the prover’s claim without learning additional information about it.

2. Non-Interactive Proof Systems:

• Non-interactive proof systems simplify this interaction. Instead of a back-and-forth communication, the prover sends a single message — the proof — which the verifier can independently check.
• The advantage of non-interactivity is significant in scenarios where ongoing interaction is impractical or impossible, such as in decentralized systems like blockchains.

3. The Role of the Fiat-Shamir Heuristic:

• The Fiat-Shamir heuristic transforms an interactive proof into a non-interactive one. It does so by replacing the role of the verifier (in issuing challenges) with a deterministic process.
• Typically, the verifier’s challenges are random and unpredictable in an interactive setting, ensuring the prover’s responses are genuine and not pre-calculated. The Fiat-Shamir heuristic substitutes these random challenges with a deterministic function, usually a cryptographic hash function.

4. Implementation of the Fiat-Shamir Heuristic:

• In practice, the prover computes a hash of the data that would have been exchanged during an interactive proof (like a concatenation of the statement to be proven and any random values chosen during the proof construction).
• The output of this hash function effectively simulates the random challenges that the verifier would have posed in an interactive setting. The prover then uses these simulated challenges to construct the proof as if they were responding to an actual verifier.
• Since cryptographic hash functions are deterministic, anyone (including the original verifier) can recompute the hash and verify that the prover’s responses correspond to the simulated challenges, thus validating the proof.

5. Advantages and Applications:

• The transformation to non-interactivity broadens the applications of zero-knowledge proofs, particularly in decentralized systems and blockchain technology, where interaction between prover and verifier is not always feasible.
• It also maintains the security properties of the interactive proofs, assuming the hash function used is secure. This means that the proofs remain sound (a false statement cannot be proven true) and zero-knowledge (no additional information is revealed about the statement being proven).

The Fiat-Shamir heuristic is a critical tool in cryptographic protocols, allowing for the practical use of zero-knowledge proofs in a wide range of applications by eliminating the need for interaction between the prover and verifier. This transformation hinges on the security and deterministic nature of cryptographic hash functions, making the proofs both efficient to verify and robust against fraud.

Comparison of the Most Popular ZKP Systems

What are SNARKs?

A complex class of zero-knowledge proofs known as “Succinct Non-Interactive Argument of Knowledge,” or “SNARKs,” allow knowledge to be proven without requiring interactive communication between the prover and the verifier. In technical terms, a SNARK is a cryptographic protocol that enables one party (the prover) to demonstrate to another party (the verifier) that a particular statement is true. The key component of the proof is that it must be brief and non-interactive, and it cannot disclose any specifics about the statement itself.

From a technical standpoint, SNARKs are characterized by:

Applications of SNARKs

• Blockchain and Cryptocurrencies: SNARKs are extensively used in blockchain technologies for creating privacy-preserving cryptocurrencies. They allow transactions to be verified without revealing any information about the transaction itself, other than its validity.
• Scalable Blockchain Networks: SNARKs enable more efficient and scalable blockchain networks by compressing transaction data, thus reducing the size of the blockchain and the resources required for transaction verification.
• Secure Voting Systems: In voting systems, SNARKs can be used to verify the validity of a vote without revealing the voter’s choice, thus ensuring both the integrity of the election and the voters’ privacy.
• Data Privacy: In various applications where data privacy is critical, SNARKs allow data verification or computations without exposing the underlying data.

What are STARKs

“Scalable Transparent Arguments of Knowledge,” or “STARKs,” are a sophisticated type of zero-knowledge cryptography proofs. Though they belong to the same category as Non-Interactive Zero-Knowledge Proofs (NIZKPs), they are differentiated by their own characteristics and underlying technology concepts.

Applications of STARKs

• Blockchain and Cryptocurrencies: STARKs are used in blockchain applications for enhancing scalability and privacy. Their ability to efficiently process large transactions while maintaining security and privacy makes them well-suited for decentralized finance (DeFi) and other blockchain-based systems.
• Data Integrity and Privacy: In fields where data integrity and privacy are paramount, such as healthcare or financial services, STARKs can verify data or transactions without revealing the underlying sensitive information.
• Cloud Computing and Data Storage: For cloud services and data storage solutions, STARKs provide a method to ensure data integrity and security, enabling verifiable computations on encrypted data.

What are Bulletproofs

A subset of non-interactive zero-knowledge proof protocols known as bulletproofs have unique characteristics that set them apart from other cryptographic proof systems such as SNARKs and STARKs. Bulletproofs, created in 2017, stand out, particularly for their effectiveness and adaptability in various cryptographic applications.

Applications of Bulletproofs

• Confidential Transactions in Cryptocurrencies: Bulletproofs are utilized in some blockchain platforms to enable confidential transactions. They allow the amount in a transaction to be hidden, enhancing privacy while ensuring the transaction is valid.
• Privacy-Enhancing Technologies: In sectors where data privacy is paramount, such as healthcare or finance, Bulletproofs provides a way to verify data or computations without exposing sensitive underlying information.
• Smart Contracts: Bulletproofs can verify certain conditions or states without revealing the details in blockchain-based smart contracts, ensuring privacy and integrity.

Comparative table for SNARKs, STARKs, and Bulletproofs

This table provides an overview of the main differences between SNARKs, STARKs, and Bulletproofs, focusing on key aspects like the need for a trusted setup, proof size, verification time, resistance to quantum computing, underlying computational assumptions, primary applications, and the nature of their interactivity.

Use Cases of ZKPs

ZKPs is a fantastic technology with a wide range of applications. Here are a few of them:

• Anonymous Credentials: Systems based on zero-knowledge proofs can be made anonymous. A user can demonstrate the possession of specific attributes (like being over a certain age or having a valid membership) in such a system without revealing their identity. This protects user privacy by enabling service providers to confirm eligibility without obtaining access to private data. In this case, zero-knowledge proofs allow the user to prove a claim without disclosing the supporting information, acting as a knowledge compression technology.
• Privacy-Preserving Smart Contracts: Zero-knowledge proofs can be utilized in the blockchain context to create privacy-preserving smart contracts. As an example, a decentralized finance (DeFi) application could serve as both a verifiable computation technology (verifying the accuracy of the vote tally without disclosing individual votes) and a knowledge compression technology (allowing voters to prove their eligibility without revealing personal information). Without disclosing the precise sums kept within their accounts. Here, zero-knowledge proofs function as verifiable computation technologies and knowledge compression, allowing users to prove statements about their assets while maintaining data privacy.
• Secure Voting: Zero-knowledge proofs can be applied to implement secure, privacy-preserving voting systems. In such systems, voters can prove their votes are valid without revealing their preferences. This ensures the integrity of the voting process while maintaining voter anonymity. Zero-knowledge proofs play a dual role in this example, as both a knowledge compression technology (allowing voters to prove their eligibility without revealing personal information) and a verifiable computation technology (verifying the correctness of the vote tally without exposing individual votes).
• Privacy-Preserving Location Services: Location-based services often require users to share their exact location, potentially infringing on privacy. Zero-knowledge proofs can be used to build privacy-preserving location services that allow users to prove their presence in a particular area or vicinity without revealing their exact coordinates. In this scenario, zero-knowledge proofs act as knowledge compression technology, enabling users to prove location-based statements without exposing their precise whereabouts. Additionally, as a verifiable computation technology, zero-knowledge proofs can ensure the correctness of the location claims without disclosing the underlying data.

Conclusion

In conclusion, zero-knowledge proofs offer a powerful tool for verifying claims without revealing sensitive information, providing numerous benefits across various industries. These include enhanced privacy, increased efficiency, and reduced costs. With new technologies like Bulletproofs, STARKs, and SNARKs, the potential applications of zero-knowledge proofs continue to expand.

From secure voting systems and privacy-preserving location services to anonymous credentials and privacy-preserving smart contracts, the versatility of zero-knowledge proofs presents exciting possibilities for innovation and growth. As technology advances, we can expect to see even more creative uses of zero-knowledge proofs, further bridging the gap between privacy and verifiability.

Originally published at https://blog.spheron.network on December 28, 2023.