Web3, with all its decentralization and user empowerment ethos, has touched a lot of innovative ideas into the digital world. Yet, with that increased transparency comes unique challenges as well, especially when it comes to privacy, complexity and let it in. This article examines the unintended privacy consequences of Web3's transparency, contrasting it with traditional finance systems and advocating for privacy-by-design solutions to empower users.
The Challenge of Transparency in Institutional Finance
Traditional financial systems have rightly been critiqued for their lack of transparency. While they still lack true anonymity, they offer the type of privacy that most Web3 use cases do not. Getting to the heart of this distinction can make all the difference in tackling the difficult issues that come with these more decentralized technologies.
Conflicts Between Full Transparency and Institutional Requirements
In traditional finance, institutions are used to working within regulatory regimes that mandate the protection of sensitive client data. Full transparency — as we’ve seen in scores of Web3 projects — is completely at odds with these regulatory necessities. These institutions have a responsibility to protect information in order to comply with laws and regulations. This is usually just not practical in the transparent and auditable ecosystem of a public blockchain. This comes together to form a huge barrier for institutional adoption of Web3 technologies.
The Need for Privacy in Financial Transactions
Privacy plays a critical role in today’s society and the financial services ecosystem. It stops other people from front-running, it protects competitive strategies, and personal financial information. In Web3, where every on-chain transaction can be traced and tracked by millions, these safeguards break down. This lack of privacy is driving off institutional investors and should be frightening retail users as well. It’s a fear that, unfortunately, limits the expansion and adoption of decentralized finance.
Addressing Privacy Concerns with Zero-Knowledge Proofs
One of the most exciting solutions to the privacy conundrum pervading Web3 are these novel cryptographic constructs we call zero-knowledge proofs (ZKPs). These cryptographic primitives allow one party to prove to another party that some claim is true. They accomplish this without revealing any details other than the fact that the statement is true or false.
How Zero-Knowledge Proofs Work
This technology allows users to verify on-chain transactions without revealing the data behind them, thus preserving privacy through zero-knowledge proofs. For example, a user can quickly prove that they have sufficient funds for a purchase. They are able to accomplish this while never revealing their true account balance. Powerful mathematical algorithms are behind the scenes facilitating all of this. So long as they are zero-knowledge, the verifiers literally don’t learn anything at all, besides that the provers statement is true.
Benefits of Implementing Zero-Knowledge Proofs in Crypto
The buzz around ZKPs in crypto is well-founded, and for good reason. It improves privacy, so users can make transactions without having their financial records laid bare. It enhances security by decreasing the chances of data breaches and unauthorized access to sensitive information. ZKPs can support regulatory compliance by empowering users to prove they satisfy specific conditions without exposing their sensitive information. This combination makes ZKPs an impactful instrument to achieve accountability and privacy in Web3.
Current Developments in Institutional Crypto Adoption
Despite these privacy difficulties, institutions have never been more optimistic about research and deploying blockchain tech. This segment aims to show you some of the most notable recent positive steps toward more institutional crypto adoption.
Institutions Actively Building on Blockchain Technology
Today, many financial institutions are already building on this distributed ledger technology, making use of the technology to improve efficiency, cut costs and strengthen security. From tokenization of assets to supply chain management to decentralized finance (DeFi), they’re diving into every use case imaginable. Complementing this, these institutions are putting large investments into research & development to find privacy-preserving solutions, which naturally fit their regulatory requirements.
AI-Driven Innovations in Financial Systems
Artificial intelligence (AI) is playing a crucial role in driving innovation in financial systems, including those built on blockchain technology. AI has the capability to analyze complex datasets at a remarkable scale and speed. By identifying patterns and automating processes, AI increases efficiency and supplements humans’ decision-making capabilities. Within the context of Web3, AI is indispensable in augmenting privacy protections. It achieves this by allowing for new data anonymization standards and advanced secure multi-party computation.
Enhancing Financial Stability with AI
AI is already being deployed to support good financial management. The technology is making risk management more proactive and fraud detection systems more efficient. This section dives into how AI can be deployed to address risks across the crypto ecosystem.
AI-Driven Liquidity Stress Testing
AI-driven liquidity stress testing would enable institutions to more accurately determine their ability to meet cash flow needs and other financial obligations during especially damaging stressed market conditions. By simulating various scenarios and analyzing the impact on liquidity, AI can identify potential vulnerabilities and inform risk management strategies. This is especially critical in the fast-moving, high-volatility crypto market where liquidity can change on a dime.
Predicting Order Book Vulnerabilities
Specifically, AI can be used to study historical data to identify potential vulnerabilities within order books. It exposes patterns that can indicate malicious manipulation or market instability. By analyzing order book activity in real-time, AI can provide a real-time flag for anomalies that might expose traders to risk. These measures would be vital in preventing market manipulation and protecting investors from losses.
Proactive Measures for a Robust Crypto Ecosystem
If we want a strong and sustainable crypto ecosystem, we need to start doing things the right way. It’s important to address privacy issues and stop bad actors. Here are a few of the proactive steps that can be seen as pushbacks.
Tracking and Flagging Movements with AI
AI could help monitor and identify increasingly complex, suspicious movements of funds in the crypto space. By analyzing transaction patterns and identifying connections between different addresses, AI can detect potential money laundering or other illicit activities. This data might be enough to raise an alarm for relevant regulatory authorities and stop additional unlawful transactions from taking place.
From Prediction to Prevention in Crypto Transactions
The long-term vision as these efforts mature is to shift from prediction to prevention in illicit crypto transactions. Using AI-driven analysis, we can get ahead of these criminal activities and help stop them before they even happen. Incorporating privacy-enhancing technologies mixes in that little extra ingredient of user privacy protection. This will take a careful joint effort between technology developers, regulatory decision-makers and industry stakeholders.
Many Web3 projects use simple authentication methods, such as asking users to sign a message using MetaMask to prove they control a blockchain address, rather than implementing the DID-Core standard. Ultimately, this approach does not provide the robustness or standardization necessary for large-scale widespread uptake.
The Web DID method, used by AT Protocol, is simple to implement but has limitations, such as relying on a URL that can be managed by a centralized entity. In other words, if the third-party website hosting a DID Document goes offline, the user’s identity is gone.
Digital decentralization and Web3 further raise difficult governance questions, from how to refine identity infrastructure, to voting systems like quadratic voting, to thriving community governance. Addressing these challenges means taking both the technical and social aspects into account.
Non-transferable “soulbound” tokens have incredible potential for cultivating social identity and allowing communities to self-govern. On privacy—which is often a red-hot concern—they can underperform. Soulbound tokens would have the unintended consequence of exposing detailed private information tied to users’ identities and affiliations.
Decentralized identity (DID) methods, such as did:web, did:ethr, and others, are being developed and implemented by emerging Web3 protocols like Ceramic Network, Web5, At Protocol, and Verida. Right now, none of the DID methods are completely up to snuff for broad implementation. The ethr-did-registry runs on top of Ethereum. It has considerable limitations: it’s cost prohibitive, time consuming and it only provides a limited DID-Core implementation with non-revocable documents.
For most schemes that offer confidentiality, it’s business as usual. There are no compliance issues. Private transfers, thanks to multimodal cryptography such as MPC and zkps, can reach compliance through cryptographic methods instead. Post-transfer selective de-anonymization minimizes the tension between blockchain privacy and regulatory compliance while still providing accountability through zk- and threshold cryptography. DID and regulatory smart contracts could encode real-world rules such as the 10K limit and other stipulations. They do this by privately exchanging data via decentralized identifiers and privacy enhancing technologies such as MPC and FHE.
Additionally, the Revoker personally flags the user’s transaction if it seems to break compliance regulations or sets off suspicious activity reports. Accountable Privacy requires users to act only for lawful purposes.
For true decentralization of Web3 applications, three key things are required:
- A DID method that meets the necessary standards for widespread adoption.
- Applications to start using a suitable DID method instead of “faking” it with blockchain-based signatures.
- Enable applications to be decentralized in the back by leveraging user encrypted data storage and other decentralized services linked to a DID document.
The real issue in Web3 though is finding the line between privacy and transparency, especially when it comes to DeFi. Techniques like post-transfer selective de-anonymization and DID and regulatory smart contracts can balance blockchain privacy and regulatory compliance using ZKPs and threshold cryptography.
