Opinion by: Casey Ford, PHD, researcher at NYM Technologies
Web3 rolled up the wave of decentralization. Decentralized applications (DAPP) increased by 74% in 2024 and individual portfolios of 485%, with total locked value (TVL) in decentralized finance (DEFI) at an almost recorded summit of $ 214 billion. The industry goes directly to a capture state directly if it does not wake up.
As Elon Musk teased her The crypto is deregulated. But when they do, is web3 ready to “protect data (user)”, as the commitment of musk substitutes? Otherwise, we are all on the verge of a global data security crisis.
The crisis comes down to vulnerability at the heart of the digital world: monitoring of metadata from all existing networks, even decentralized decentralized Web3. AI technologies are now at the foundation of surveillance systems and serve as an accelerator. Anonymity networks offer a way to get out of this capture state. But it must start with metadata protections at all levels.
Metadata is the new border of surveillance
Metadata is the neglected raw material of AI monitoring. Compared to payload data, metadata is light and therefore easy to treat en mass. Here, the Systems have excellent better. Aggregated metadata can reveal much more than encrypted content: behavior models, contact networks, personal desires and, ultimately, predictability. And legally, it is not protected in the way in which the encrypted (E2E) Communications are now in certain regions.
While metadata are part of all digital assets, metadata that discloses encrypted traffic E2E exposes us and what we do: IPS, synchronization signatures, package sizes, encryption formats and even portfolio specifications. All this is fully readable for opponents to monitor a network. Blockchain transactions are no exception.
Tas of digital waste can emerge a gold mine of discs detailed from everything we do. The metadata is our digital unconscious, and it is to be won so that the machines can harvest it for profit.
The limits of the blockchain
The protection of transaction metadata was a reflection after the blow of blockchain technology. The crypto does not offer anonymity Despite the reactionary association of industry with illicit trade. It offers pseudonymatThe possibility of holding tokens in a wallet with a chosen name.
Recent: How to donize real world active ingredients on Bitcoin
Harry Halpin and Ania Piotrowska have diagnostic The situation:
“(T) the public nature of the big book of Bitcoin transactions (…) means that anyone can observe the flow of parts. (P) The seudonym addresses do not provide any significant anonymity level, because anyone can harvest the counterpart addresses of a given transaction and reconstruct the chain of transactions. »»
As all chain transactions are public, anyone running a complete node can have a panoptic view of the chain activity. In addition, metadata of metadata joined with pseudonym portfolios can be used to identify people’s locations and identities if follow -up technologies are sufficiently sophisticated.
This is the central problem of monitoring metadata in the blockchain economy: surveillance systems can effectively decrease our financial traffic by any capable part.
Knowledge is also an insecurity
Knowledge is not only power, as the adage says. It is also the base on which we are exploited and unpleasant. There are at least three general risks of metadata on web3.
Fraud: financial insecurity and surveillance are intrinsically linked. The most serious hacks, flights or scams depend on knowledge accumulated on a target: their assets, their transactions stories and which they are. Dappradar estimates a loss of $ 1.3 billion due to “hacks and exploits” as phishing attacks in 2024 only.
Leaks: The portfolios that allow access to decentralized tokenomics are based on centralized infrastructure that fupend. Studies DAPPs and portfolios have shown the prevalence of IP leaks: “The existing portfolio infrastructure is not in favor of user confidentiality. Websites abuse portfolios for online fingerprint users, and DAPPS and wallets disclose the address of the user’s portfolio to third parties. Pseudonymat is useless if people identities and transaction models can be easily revealed through metadata.
Chain consensus: chain consensus is a potential point of attack. An example is a recent Celestia initiative to add an anonymity layer to obscure the metadata of validators against specific attacks seeking to disrupt chain consensus in the process of sampling the availability of Celestia data (DAS).
Secure web3 via anonymity
While web3 continues to grow, the quantity of metadata concerning people’s activities is offered to newly authorized monitoring systems.
Beyond VPN
Virtual Private Network (VPN) technology has decades at this stage. The lack of advancement is shocking, most of the VPNs remaining in the same centralized and owners infrastructure. Networks like Tor and Dandelion intervened as decentralized solutions. However, they are always vulnerable to surveillance by global opponents capable of “synchronization analysis” through control of the entry and exit nodes. Even more advanced tools are necessary.
Noise networks
All surveillance is looking for models in a network full of noise. By obscuring other models of communication and disintegration metadata such as IP from metadata generated by circulation, possible attack vectors can be considerably reduced and metadata models can be blurred in nonsense.
Anonymization networks have appeared to anonymize sensitive traffic such as communications or cryptographic transactions via noise: cover traffic, synchronization obscurities and data mixture. In the same spirit, other VPNs like Mullvad have introduced programs like Daita (defense against the analysis of traffic guided by AI), which seeks to add “distortion” to its VPN network.
Blur the codes
Whether it is a question of defending people against the assassinations in Drone Wars of tomorrow or of securing their onchain transactions, new anonymity networks are necessary to blur the codes of what makes us all target: the metadata that our online life leaves in their wake.
The capture state is already there. Automatic learning feeds on our data. Instead of leaving people’s data, unprotected systems, web3 and anonymity can ensure that what ends in AI teeth is effectively garbage.
Opinion of: Casey Ford, PHD, researcher at NYM Technologies.
This article is for general information purposes and is not intended to be and must not be considered as legal or investment advice. The points of view, the thoughts and opinions expressed here are the only of the author and do not reflect or do not necessarily represent the opinions and opinions of Cointellegraph.
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