Biblio

Advancement in network technology provides an opportunity for e-commerce industries to sell digital content. However, multimedia content has the drawback of easy copy and redistribution, which causes rampant piracy. Digital rights management (DRM) systems are developed to address content piracy. Basically, DRM focuses to control content consumption and distribution. In general, to provide copyright protection, DRM system loses flexibility and creates a severe threat to users’ privacy. Moreover, traditional DRM systems are client-server architecture, which cannot handle strategies geographically. These disadvantages discourage the adoption of DRM systems. At the same time, multi-distributor DRM (MD-DRM) system provides a way to facilitate content distribution more effectively. Most of the existing multi-distributor DRM systems are privacy encroaching and do not discuss the useful content distribution framework. To overcome the drawbacks of existing schemes, we propose a privacy-preserving MD-DRM system, which is flexible enough to support location-based content distribution. The proposed scheme maintains a flexible and transparent content distribution without breaching consumer privacy. Besides, the proposed scheme does not violate accountability parameters. This mechanism makes traitor identification possible without violating the privacy rights of authorized consumers.

The maturity of intelligent transportation system, cloud computing and Internet of Things (IoT) technology has encouraged the rapid growth of vehicular ad-hoc networks (VANETs). Currently, vehicles are supposed to carry relatively more storage, on board computing facilities, increased sensing power and communication systems. In order to cope with real world demands such as low latency, low storage cost, mobility, etc., for the deployment of VANETs, numerous attempts have been taken to integrate fog-computing with VANETs. In the recent past, Ma et al. (IEEE Internet of Things, pp 2327-4662, 10. 1109/JIOT.2019.2902840) designed “An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks”. Ma et al. claimed that their protocol offers secure communication in fog-based VANETs and is resilient against several security attacks. However, this comment demonstrates that their scheme is defenseless against vehicle-user impersonation attack and reveals secret keys of vehicle-user and fog-node. Moreover, it fails to offer vehicle-user anonymity and has inefficient login phase. This paper also gives some essential suggestions on strengthening resilience of the scheme, which are overlooked by Ma et al.

The convergence of Internet of Things (IoT) and cloud computing is due to the practical necessity for providing broader services to extensive user in distinct environments. However, cloud computing has numerous constraints for applications that require high-mobility and high latency, notably in adversarial situations (e.g. battlefields). These limitations can be elevated to some extent, in a fog computing model because it covers the gap between remote data-center and edge device. Since, the fog nodes are usually installed in remote areas, therefore, they impose the design of fool proof safety solution for a fog-based setting. Thus, to ensure the security and privacy of fog-based environment, numerous schemes have been developed by researchers. In the recent past, Jia et al. (Wireless Networks, DOI: 10.1007/s11276-018-1759-3) designed a fog-based three-party scheme for healthcare system using bilinear. They claim that their scheme can withstand common security attacks. However, in this work we investigated their scheme and show that their scheme has different susceptibilities such as revealing of secret parameters, and fog node impersonation attack. Moreover, it lacks the anonymity of user anonymity and has inefficient login phase. Consequently, we have suggestion with some necessary guidelines for attack resilience that are unheeded by Jia et al.

Named Data Network (NDN) is an alternative to host-centric networking exemplified by today's Internet. One key feature of NDN is in-network caching that reduces access delay and query overhead by caching popular contents at the source as well as at a few other nodes. Unfortunately, in-network caching suffers various privacy risks by different attacks, one of which is termed timing attack. This is an attack to infer whether a consumer has recently requested certain contents based on the time difference between the delivery time of those contents that are currently cached and those that are not cached. In order to prevent the privacy leakage and resist such kind of attacks, we propose a detection scheme by adopting Long Short-term Memory (LSTM) model. Based on the four input features of LSTM, cache hit ratio, average request interval, request frequency, and types of requested contents, we timely capture more important eigenvalues by dividing a constant time window size into a few small slices in order to detect timing attacks accurately. We have performed extensive simulations to compare our scheme with several other state-of-the-art schemes in classification accuracy, detection ratio, false alarm ratio, and F-measure. It has been shown that our scheme possesses a better performance in all cases studied.