The integration of the Internet of Things (IoT) and cloud computing has become an attractive cloud-oriented big data processing paradigm, which is playing an important role in efficiency and productivity for digitalization of numerous IoT enabled industries. However, cloud-assisted IoT is also becoming an increasingly attractive target for various cyber-attacks, including the authenticity of outsourcing data, untrustworthiness of third parties, and data security and privacy. As a potential and promising solution to securely outsource data, we present the first construction for an efficient cloud data outsourcing system with simultaneous individual transmission preserving outsider anonymity of the subscribed consumer set. In our system, a data owner generates personalized data for each of the consumers in a group and transmits a common encrypted data for the group in such a way that the subscribed consumer set is completely hidden from the outsiders. Personalized data can be recovered only by an authorized consumer, while the common data can be decrypted by all the authorized consumer in that group. The communication bandwidth is compact in our construction, and the decryption algorithm requires significantly less computation cost. We design our scheme using asymmetric bilinear map over the prime order group to prevent fault attacks on symmetric bilinear map. Our construction is built in identity-based setting without any non-standard $q$-type security assumption and does not use random oracles. Our scheme enjoys adaptive security against an indistinguishable chosen-plaintext attack under the hardness of the standard decisional bilinear Diffie–Hellman exponent problem. Furthermore, our design supports an exponential number of consumers as the size of the valid identity set grows exponentially with the security parameter, whereas it is only polynomial in the security parameter for the existing cloud data outsourcing systems. In particular, the implementation and performance analysis explicates the advantages of our design for resource-constrained IoT enabled frameworks.

物联网（IoT）和云计算的集成已成为一种有吸引力的面向云的大数据处理范例，它在众多支持物联网的行业数字化的效率和生产率中发挥着重要作用。但是，基于云的物联网也正成为各种网络攻击的越来越有吸引力的目标，包括外包数据的真实性，第三方的不信任以及数据安全性和隐私性。作为安全地外包数据的潜在且有希望的解决方案，我们提出了一种高效的云数据外包系统的首个结构，该系统同时进行个人传输并保持外部匿名所订阅的消费者集合中的一个。在我们的系统中，数据所有者为组中的每个消费者生成个性化数据，并以一种方式传输组的公共加密数据，以使订阅的消费者集对外界完全隐藏。个性化数据只能由授权使用者恢复，而通用数据可以由该组中的所有授权使用者解密。在我们的结构中，通信带宽是紧凑的，并且解密算法需要显着更少的计算成本。我们在素数阶组上使用非对称双线性图来设计我们的方案，以防止对对称双线性图进行故障攻击。我们的建筑以基于身份的环境建造，没有任何非标准$q$-类型的安全性假设，并且不使用随机预言。我们的方案在标准决策双线性Diffie-Hellman指数问题的难度下，针对不可区分的明文攻击享有自适应安全性。此外，我们的设计支持了指数的消费者数量的有效身份证集的大小与安全参数呈指数增长，而这仅仅是在现有的云计算数据外包系统安全参数多项式。尤其是，实施和性能分析充分说明了我们针对资源受限的IoT启用框架设计的优势。