The telecare medical information systems (TMIS) provide the convenience to the patients/users to be served at home. Along with such ease, it is essential to preserve the privacy and to provide the security to the patients/users in TMIS. Often, authentication protocols are adopted to guarantee privacy and secure interaction between the patients/users and remote server. Recently, Chaudhry et al. pointed out that Islam et al.’s scheme based on smart card is prone to user impersonation and server impersonation attacks. Chaudhry et al. later presented a enhanced scheme based on Elliptic curve cryptography (ECC) to remedy the weaknesses of Islam et al.’s scheme. Unfortunately, we find some important limitations in both schemes. We remark that their scheme is prone to off-line password guessing attack, user/server impersonation attack and man-in-middle attack. To overcome these limitations, we present an improved authentication scheme keeping apart the threats encountered in the design of Chaudhry et al.’s scheme. Moreover, the presented scheme can also resist all known attacks. We prove the security of the proposed scheme with the help of widespread Burrows-Abadi-Needham Logic (BAN-Logic). A brief comparison with the previous works provides that the presented protocol is more efficient and more secure than other related schemes.

The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks’ traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading’s category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.

The privacy of users must be considered as the utmost priority in distributed networks. To protect the identities of users, attribute-based encryption (ABE) was presented by Sahai et al. ABE has been widely used in many scenarios, particularly in cloud computing. In this paper, public key encryption with equality test is concatenated with key-policy ABE (KP-ABE) to present KP-ABE with equality test (KP-ABEwET). The proposed scheme not only offers fine-grained authorization of ciphertexts but also protects the identities of users. In contrast to ABE with keyword search, KP-ABEwET can test whether the ciphertexts encrypted by different public keys contain the same information. Moreover, the authorization process of the presented scheme is more flexible than that of Ma et al.'s scheme. Furthermore, the proposed scheme achieves one-way against chosen-ciphertext attack based on the bilinear Diffie-Hellman (BDH) assumption. In addition, a new computational problem called the twin-decision BDH problem (tDBDH) is proposed in this paper. tDBDH is proved to be as hard as the decisional BDH problem. Finally, for the first time, the security model of authorization is provided, and the security of authorization based on the tDBDH assumption is proven in the random oracle model.

Online social databases are rich sources to retrieve appropriate information that is subsequently analyzed for forthcoming trends prediction. In this paper, we identify rising stars in cricket domain by employing machine learning techniques. More precisely, we predict rising stars from batting as well as from bowling realms. For this intent, the concepts of co-players, team, and opposite teams are incorporated and distinct features along with their mathematical formulations are presented. For classification purpose, generative and discriminative machine learning algorithms are employed, and two models from each category are evaluated. As a proof of applicability, the proposed approach is validated experimentally while analyzing the impact of individual features. Besides, model and categorywise assessment is also performed. Employing cross validation, we demonstrate high accuracy for rising star prediction that is both robust and statistically significant. Finally, ranking lists of top ten rising cricketers based on weighted average, performance evolution, and rising star scores are compared with the international cricket council rankings.