for data security and privacy using blockchain [part I] (关于区块链与数据安全和隐私)

纵观当前互联网的发展阶段,client 与大的集中式company之间确实越来越不对等,
client需要使用company提供的服务functionality,而互联网公司【尤其是那些以广告为主要商业模式的】需要用户的data来完成盈利或者提供更好的服务。但是矛盾主要是一旦data被company采集之后用户就完全的丧失了对于其数据【含有个人private、confidential info】的控制,company如何使用这些数据就完全是一个blockbox了。

传统的密码学researcher可以很明显的将上述问题抽象为安全计算的问题pattern。并且可以从理论上使用FHE、Garbled circuit等SMC工具来尝试解决这一问题,但是问题是这样的解决方案由于其本身的假设就是过于侧重security,所以efficiency在当前的时代是无法让人满意的,很不具有实用性。我个人的判断是即使FHE等技术的效率可以得到不断的提升,也很难大规模的得到通用的使用。但是其对于cryptographical security的假设之于实际问题恰类似于 香农当年的semantic security定义下perfect informational security的解决方案一样难以实际的carry out。而后来恰是DH 在经典论文中提出的以computational security的定义为基础的全新的【弱化了但仍然足够practical】安全假设才使得这一问题的解决方案的方向得到了修正,从而启发了后续整个公钥、现代密码学的发展。

在21世纪后,有人看到了privacy问题和security问题的不同,所以Dwork试图按照类似的思路提出了differential privacy的新的安全假设定义,并基于此给出了Laplace mechanism等具体的方案构造。这一定义从本质内涵上确实与indistinguishable 的computational security的定义非常相近,但是问题在于其虽然理论上很elegant,但是在具体的某一functionality之下根据其定义中的sensitivity,很可能会导致构造的noise过大,从而使得需要满足这一安全定义则数据的可用性utility受损,进而不实用。另一方面是,如果不考虑基于random response思想为基础的Local DP技术(注意可以证明在某些构造下其也满足DP的定义),其整体的模式仍然是中心化的,其保证的是output中的信息至于input的隐私性,而非SMC所强调的计算过程本身之于input的安全性。【所以可以看到有些researcher也是看到了这一点,将两者加以了结合】。


所以针对实际的big problem,是否从技术上就是完全的无解呢? 之前看过的纯理论的paper中有对于program obfuscation的negative的理论证明,但其证明方法是构造式的,即通过构造了一个极端的function来说明无法通过同态加密等方式无法保证既定的安全定义。所以说从实际的角度上看,这样的结果对于我们寻找实际practical的技术解决方案并无大的影响。
【注1】

bitcoin是08年正式论文发表的,而blockchain技术是最近几年才从数字货币这一上层应用中作为通用的底层被抽象出来的。基于incentive式的PoW方式来解决分布式一致性问题,可以提供去中心化、不可篡改、全程可追踪等特性,从而可以提供更上层的可信任等功能。

区块链本身显然不是为了解决安全或者隐私的问题而设计出来的,所以在考虑使用区块链技术来进行非数字货币方面的去中心化应用时我们都必须得非常小心的去考虑这样是否是合适的。

另一方面,虽然不是这样的目的,但其给我们提供了一些进行密码学应用时的一些新的思路,即或许我们从理论上难以进行更恰好的一个relxed 安全或隐私的定义了,但是能否可以从system、从已经具有了Internet等这些新的现实基础设施的这些实际的客观现实上面去进行structural方面的构造和探索,而不是像古希腊的哲人所走的那条虽然很优美但是显然也是很受限的思维之路那样--只考虑了纯粹的基于抽象数学逻辑推理的理性而忽略了后来的达尔文等科学家所同样重视的观察、实验等技术的重要性。

所以结论是什么呢? 我自己,或者说更多的和我有相同想法的人群,在当前的阶段,我们应该更多的以practical的心态,去尝试利用一些现实已有的客观事物(就如bitcoin中对P2P网络的应用那样),结合偏纯逻辑的密码学理论等技术工具,尝试创造性的构造真正的解决我们当前时代所面临的这个big problem!

比如,是否是可以基于blockchain这样的基础设置构建类似于哲学中的抽象gods的role【如同密码学中对于ideal world中的相似角色的构造】,从而别的Dapp真的可以基于此为source来进行可靠地数据的providing和功能上的使用?

【备注:
1,我们同时需要承认,在非常limited的场景下,上述无论是SMC还是central DP都可以发挥很好的作用,只是说在稍微general的functionality下,其可能就不是work了】
下载方式:https://pan.quark.cn/s/a4b39357ea24 在纺织制造领域中,纱线的品质水平对最终制成品的整体质量具有决定性作用。 鉴于消费者对于产品规格样式要求的不断变化,纺织制造工艺的执行过程日益呈现为一种更为复杂的操作体系,进而导致对纱线质量进行预测的任务变得更加困难。 在众多预测技术中,传统的预测手段在面对多变量间相互交织的复杂关系时,往往显得力不从心。 因此,智能计算技术在预测纱线质量的应用场景中逐渐占据核心地位,其中人工神经网络凭借其卓越的非线性映射特性以及自适应学习机制,成为了众多预测方法中的一种重要选择。 在智能计算技术的范畴内,粒子群优化算法(PSO)反向传播神经网络(BP神经网络)是两种被广泛采用的技术方案。 粒子群优化算法是一种基于群体智能理念的优化技术,它通过模拟鸟类的群体觅食行为来寻求最优解,该算法因其操作简便、执行高效以及具备优秀的全局搜索性能,在函数优化、神经网络训练等多个领域得到了普遍应用。 反向传播神经网络则是一种由多层节点构成的前馈神经网络,它通过误差反向传播的机制来实现网络权重阈值的动态调整,从而达成学习预测的目标。 在实际操作层面,反向传播神经网络因其架构设计简洁、实现过程便捷,因此被广泛部署于各类预测分类任务之中。 然而,该方法也存在一些固有的局限性,例如容易陷入局部最优状态、网络收敛过程缓慢等问题。 而粒子群优化算法在参神经网络优化时,能够显著增强神经网络的全局搜索性能并提升收敛速度,有效规避神经网络陷入局部最优的困境。 将粒子群优化算法反向传播神经网络相结合形成的PSO-BP神经网络,通过运用粒子群优化算法对反向传播神经网络的权值阈值进行精细化调整,能够在预测纱线断裂强度方面,显著提升预测结果的...
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