Vitalik Buterin 表示混淆技术可以增强区块链隐私

TheNewsCryptoPublished on 2026-06-29Last updated on 2026-06-29

Abstract

以太坊联合创始人Vitalik Buterin近日发表文章,探讨了混淆技术在加密行业的应用前景。他指出,密码学混淆技术有望在未来增强区块链的隐私和安全性。混淆将普通代码转换为加密形式,同时保持其输出不变,从而保护代码本身不被分析,使开发者能够在不泄露专有代码或商业逻辑的情况下构建应用。 Buterin认为,单纯依靠混淆技术不足以安全处理加密货币等数字资产,因为用户可以复制混淆后的代码。然而,区块链网络通过分布式账本技术记录了所有权和交易,与混淆技术结合后,可以创建既能证明所有权又能隐藏程序逻辑的应用。这种结合有望用于安全的支付系统、金融应用、机密商业运营等场景。 不过,混淆技术的实际应用仍面临重大技术障碍。研究人员在“不可区分混淆”领域已取得进展,但现有实现方案的效率极低,某些方法的执行时间甚至超过宇宙寿命。目前,学界正在研究优化密码学方法、改进数学工具以及探索全新方案,以提升算法效率。 Buterin总结称,实用的混淆技术尚需时日才能实现,但随着技术进步,未来有望打造出无需信任第三方的高安全性区块链产品。

以太坊联合创始人 Vitalik Buterin 发表了一篇全面阐述加密货币行业中混淆技术的文章。他解释了密码学混淆技术未来如何能够增强区块链的隐私性和安全性。混淆技术将普通代码转换为加密形式,同时允许其产生相同的输出。

与保护存储或传输信息的加密技术不同,混淆技术保护的是代码本身。这样做是为了防止任何试图查看和理解其工作原理的用户进行分析。

这将使开发人员能够使用保持安全的信息开发应用程序,而无需泄露任何专有代码或商业逻辑。他表示,混淆技术与区块链技术相结合,可以创建能够保护用户隐私的系统,同时减少对中心化权威机构服务的依赖。

来源:Vitalik.eth

区块链可以增强混淆技术的能力

单独的混淆技术不足以安全地处理像加密货币这样的数字资产,因为用户可以简单地复制混淆。因此,仅通过混淆技术来处理余额和所有权是不可能的。

然而,区块链网络固有地具有以分布式账本技术形式记录所有权和交易的能力,这使得它与混淆技术结合时,适合创建能够证明所有权同时隐藏程序逻辑的应用程序。正如 Buterin 所言,这种结合将实现安全的支付系统、金融应用、机密业务运营以及许多其他区块链应用。

技术困难阻碍混淆技术的实际应用

Buterin 表示,研究人员在一个称为不可区分混淆的领域取得了相当大的进展,该领域旨在使外部观察者无法区分产生相同输出的不同程序。

虽然研究人员已经证明,在假定某些公认的安全级别下,这种混淆是可能的,但这些实现的效率极低。根据 Buterin 的说法,目前采用的某些技术资源消耗如此之大,以至于它们的执行时间将超过宇宙的寿命。

科学家们仍在研究几种有助于提高算法效率的技术。其中包括优化当前使用的密码学方法、改进数学工具,甚至采用现有框架之外的全新方法。

Buterin 总结说,实用的混淆技术还需要相当长的时间才能变得可行,但进一步的改进将使得创建高度安全、无需任何可信第三方参与的区块链产品成为可能。

加密货币新闻要点:

随着稳定币供应收紧,印度 USDT 溢价飙升至 8.5% 以上

标签区块链以太坊 (ETH)Vitalikvitalik ButerinVitalikButerin

Trending Cryptos

Related Questions

QVitalik Buterin 在文章中提到混淆技术可以如何增强区块链的隐私和安全性?

AVitalik Buterin 指出,密码学混淆技术可以通过将普通代码转换为加密形式来保护代码本身,防止他人分析其工作原理。与加密存储或传输的信息不同,混淆技术直接作用于代码。当与区块链技术结合时,可以创建出能保护用户隐私、同时减少对中央权威机构依赖的系统。

Q为什么单纯的混淆技术不足以安全地处理像加密货币这样的数字资产?

A单纯的混淆技术不足以安全处理加密货币等数字资产,因为用户可以简单地复制混淆后的代码。这意味着仅靠混淆无法可靠地管理和验证资产的余额和所有权,因为底层的逻辑可以被复制和重复使用。

Q区块链技术如何弥补单纯混淆技术的不足,以创建更强大的应用?

A区块链网络具有通过分布式账本技术记录所有权和交易的内在能力。当与混淆技术结合时,就可以创建出能够证明资产所有权(通过区块链的不可篡改记录),同时隐藏具体程序逻辑(通过混淆技术)的应用程序,从而实现更安全和私密的支付系统、金融应用和商业运营。

Q目前阻碍混淆技术实际应用的主要技术难点是什么?

A阻碍混淆技术实际应用的主要技术难点是实现效率极低。尽管研究人员已经在‘不可区分性混淆’领域取得了进展,并证明了其理论上的可能性,但现有的实现方法在计算资源上消耗巨大,某些技术的执行时间甚至可能超过宇宙的寿命,因此目前完全不具备实用性。

QVitalik Buterin 对未来混淆技术与区块链结合的前景持什么看法?

AVitalik Buterin 认为,实用的混淆技术还需要相当长的时间才能变得可行。但他也指出,通过优化现有密码学方法、改进数学工具以及探索全新方法等研究,未来的进步将能够创造出高度安全、无需任何受信任第三方参与的基于区块链的产品。

Related Reads

You Use Claude and Codex Every Day, but Meta Has Restricted Internal Use

In May, Meta imposed internal restrictions on its engineers regarding the use of Claude Code and Codex, two widely used AI programming tools. Despite being a major client, Meta's guidelines, still in effect, prohibit these external models from being used for specific tasks to prevent potential "escalations with partners." The core concern is "distillation"—the risk that outputs from Claude or Codex could inadvertently contaminate the training data and evaluation processes for Meta's in-house AI coding assistant, MetaCode. If MetaCode is trained or evaluated using data generated by these external models, it risks learning their capabilities rather than developing its own, blurring the line of intellectual origin. The restrictions are precise: engineers cannot use the external models to generate test questions, debug source code, or suggest test cases. AI-generated content is also barred from environments accessible to MetaCode. However, AI can still assist with peripheral tasks like workflow setup and code organization, provided all outputs are manually reviewed. This caution reflects a broader industry dilemma. While distillation is a common technique, using a competitor's model output for training raises legal and ethical questions about the ownership of derived capabilities. Contractual terms from companies like OpenAI and Anthropic explicitly forbid using their outputs to build competing products, putting enforcement power in the hands of rivals. The move is also financially motivated, as Meta seeks to reduce its hefty internal AI spending, estimated in the billions this year. Meta's policy illustrates the delicate balance companies must strike: leveraging powerful external AI tools while safeguarding the integrity and independence of their own AI development. As AI systems increasingly help build other AIs, distinguishing the origin of capabilities becomes a fundamental challenge for the entire industry.

marsbit2h ago

You Use Claude and Codex Every Day, but Meta Has Restricted Internal Use

marsbit2h ago

Why Do We Need an AI Content Perspective Today?

The article "Why Do We Need an AI Content Perspective Today?" explores the complex and often contentious integration of AI into the cultural and creative industries, particularly film and television. It begins with the cancellation of Amazon's AI-generated animation "Punky Duck," highlighting the ethical debates surrounding AI content. AI's rapid advancement is transforming video production, enabling cost-effective, full-length AI films (e.g., "RAPHAEL," "Dreams of Violets") while sparking industry resistance over issues like "synthetic actors." The core debate has shifted from whether to use AI to how to use it responsibly. The article analyzes why AI's entry into film is uniquely unsettling. It distinguishes between "cultural fast food" (short-form, fast-paced content like micro-dramas) and "cultural main courses" (traditional, long-form film/TV). AI currently excels at the former, matching its fragmented narratives, shallow emotional needs, and free-to-consumer models. However, venturing into the latter challenges the human-centric essence of storytelling—creativity, emotional depth, and the unique value of human labor and experience. While AI can generate massive volumes of content and lower costs, it risks devaluing human creativity, leading to homogenized output, and creating unfair competition through potential intellectual property infringement. Its efficiency also amplifies content safety risks, making preemptive governance crucial. To counter these risks, the article proposes establishing clear boundaries guided by a human-centered AI content perspective. It outlines four principles: 1) Amplify, rather than displace, human creative space; 2) Respect and protect human creative output; 3) Ensure human creative control and responsibility remain paramount; and 4) Guarantee transparency and traceability in AI creation. The conclusion emphasizes that humans must act as the "helmsmen" of technology, steering AI development to enhance, not replace, the core human values at the heart of cultural expression.

marsbit2h ago

Why Do We Need an AI Content Perspective Today?

marsbit2h ago

Trading

Spot

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of ETH (ETH) are presented below.

活动图片