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Recently, Named Data Networking (NDN) has emerged as a popular and active Internet architecture that addresses the issues of current host-centric communication. NDN is well suited for Internet of Things (IoT) which possesses massive applications that dominate the Internet today. It intends to provide named-based routing, in-networking caching, built-in mobility and multicast support as part of its design which leads to a substantial improvement in content delivery/retrieval. Though, this new architecture aches from some new challenges in terms of security. In this article, we seek our attention towards Content Poisoning Attack (CPA). The purpose of CPA is to inject poisoned content with an invalid signature into the NDN-based IoT networks. Unfortunately, none of the existing proposals work effectively when malicious attackers compromise the caches of NDN routers. To prevent this, we proposed a certificateless signature scheme for the preservation of CPA in NDN-based IoT networks. The proposed scheme is formally secure under the security hardness of Hyperelliptic Curve Discrete Logarithm Problem (HCDLP) with a security simulation/validation in "Automated Validation of Internet Security Protocols and Applications (AVISPA)". Besides, the formal proof we also compared the designed scheme with some existing solutions to show the cost-efficiency in terms of communication overhead and computation cost. To conclude, a robust deployment on NDN-based IoT networks is shown.

Perceptual encryption (PE) of images protects visual information while retaining the intrinsic properties necessary to enable computation in the encryption domain. Block–based PE produces JPEG-compliant images with almost the same compression savings as that of the plain images. The methods represent an input color image as a pseudo grayscale image to benefit from a smaller block size. However, such representation degrades image quality and compression savings, and removes color information, which limits their applications. To solve these limitations, we proposed inter and intra block processing for compressible PE methods (IIB–CPE). The method represents an input as a color image and performs block-level inter processing and sub-block-level intra processing on it. The intra block processing results in an inside–out geometric transformation that disrupts the symmetry of an entire block thus achieves visual encryption of local details while preserving the global contents of an image. The intra block-level processing allows the use of a smaller block size, which improves encryption efficiency without compromising compression performance. Our analyses showed that IIB–CPE offers 15% bitrate savings with better image quality than the existing PE methods. In addition, we extended the scope of applications of the proposed IIB–CPE to the privacy-preserving deep learning (PPDL) domain.

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