Recently, a variety of novel techniques (e.g. Internet-of-Things, cloud computing, edge/fog computing, big data, intelligence accelerating chip) make a great number of different devices connected for specific purposes. Based on the significant features of techniques, networking technologies have evolved into future intelligent networks (FINs), in which intelligence has been integrated into networks to help generate and optimize policies, freeing network administrators from management and configuration burdens, and improving the efficiency of self-learning from real-time network data. In FINs, low latency is achieved at the cost of computing-complexity which is beyond the capabilities of Internet of Things devices or users' devices. In order to achieve a new generation of computing-intensive, delay-sensitive and function-intelligent services, computing-intensive intelligence tasks are expected to be offloaded to more powerful edge devices with intelligent computing capabilities. However, because the data are copied or divided before being distributed to edge devices, and that the edge devices have heterogeneous computation resources and various purposes, there exist unknown types of security and privacy threats which would possibly crash the network system, break the data privacy of network entities, damage the data property or cause unfairness in incentives adjustment. In this article, we discuss the design issues for data security and privacy in FINs. We present the unique data security and privacy design challenges presented by computing offloading and highlight the reasons why the data protection techniques in current Internet-of- Things, cloud computing, edge/fog computing cannot be directly applied in FINs.

中文翻译：

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FIN 中计算卸载的数据安全和隐私挑战


近年来，各种新技术（例如物联网、云计算、边缘/雾计算、大数据、智能加速芯片）使大量不同的设备出于特定目的而连接起来。基于技术的显着特征，网络技术已演进为未来智能网络（FIN），将智能融入网络，帮助生成和优化策略，将网络管理员从管理和配置的负担中解放出来，提高自身的效率。 -从实时网络数据中学习。在FIN中，低延迟是以计算复杂性为代价实现的，这超出了物联网设备或用户设备的能力。为了实现新一代计算密集型、时延敏感型和功能智能型服务，计算密集型智能任务有望被卸载到更强大的具有智能计算能力的边缘设备上。然而，由于数据在分发到边缘设备之前被复制或分割，并且边缘设备具有异构计算资源和不同用途，因此存在未知类型的安全和隐私威胁，可能导致网络系统崩溃，破坏数据隐私损害网络主体的数据财产或造成激励调整的不公平。在本文中，我们讨论 FIN 中数据安全和隐私的设计问题。我们提出了计算卸载带来的独特的数据安全和隐私设计挑战，并强调了当前物联网、云计算、边缘/雾计算中的数据保护技术不能直接应用于FIN的原因。