The recent advent of cloud computing and the IoT has made it imperative to have efficient and secure cryptographic schemes for online data sharing. Data owners would ideally want to store their data/files online in an encrypted manner, and delegate decryption rights for some of these to users with appropriate credentials. An efficient and recently proposed solution in this regard is to use the concept of aggregation that allows users to decrypt multiple classes of data using a single key of constant size. In this paper, we propose a secure and dynamic key aggregate encryption scheme for online data sharing that operates on elliptic curve subgroups while allowing dynamic revocation of user access rights. We augment this basic construction to a generalized two-level hierarchical structure that achieves optimal space and time complexities, and also efficiently accommodates extension of data classes. Finally, we propose an extension to the generalized scheme that allows use of efficiently computable bilinear pairings for encryption and decryption operations. Each scheme is formally proven to be semantically secure. Practical experiments have been conducted to validate all claims made in the paper.

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Linear layers are crucial building blocks in the design of lightweight block ciphers, since they perform the dual task of providing the much needed diffusion, while also ensuring minimal hardware cost for implementation. Although a number of lightweight block ciphers with parsimoniously designed linear layers have been proposed in cryptographic literature, there is limited work on generic construction techniques for such linear layers, to the best of our knowledge. The challenge in designing a suitable linear layer, that combines the requirements of both cryptographic strength and lightweightedness, lies in the huge search space accompanying such a construction technique. In this paper, we propose a hierarchical linear layer construction technique that systematically combines the principles of block interleaving and wide trail design strategy to construct large linear layers from suitably chosen smaller linear layers that guarantee the necessary diffusion properties. Additionally, the smaller linear layers are realized by iterating linear layers which are extremely lightweight, thus providing us with a strategy to guarantee diffusion while ensuring that the gate count of the design is minimized. In order to demonstrate the efficiency of our proposed technique, we compare it with the general construction technique proposed for the design of the block cipher PRIDE. To the best of our knowledge, PRIDE offers the only other general construction technique that focuses specifically on the construction of lightweight linear layers. While the construction technique of PRIDE is efficient for software implementations, our technique provides 60% and 50% greater savings in terms of area footprint on ASIC and FPGA based designs respectively, with an overall area-time product reduction by 7.5%. The main contribution of this work lies in providing the cipher design community with a generic off-the-shelf technique for designing lightweight linear layers with high diffusion for hardwareoriented applications.

Security is a critical aspect in many of the latest embedded and IoT systems. Malware is one of the severe threats of security for such devices. There have been enormous efforts in malware detection and analysis; however, occurrences of newer varieties of malicious codes prove that it is an extremely difficult problem given the nature of these surreptitious codes. In this article, instead of addressing a general solution, we aim at malware detection for platforms that have more than one core for performance enhancement. We investigate the utility of multiple cores from the point of view of security, where one of the cores operate as a watchdog. We define a notion of a new metric called LAMBDA (Lightweight Assessment of Malware for emBeddeD Architectures), denoted by λ, indicating a conceptual boundary between the programs which are allowed to run on a given platform, with the codes that are suspected as malwares. The metric λ is computed using carefully chosen monitors or features, which are tuples of high-level programs representing OS resources, along with low-level hardware performance counters. In comparison to heavy-weight machine learning techniques, we use an online hypothesis testing, in the form of t -test, to classify a given program-under-test. For applications where security is of prime concern, we propose an additional step based on multivariate analysis to classify the unknown programs that are closer to the threshold with a high degree of confidence. We present experimental results focusing on an ARM-based platform which validate that the proposed approach provides a lightweight, accurate assessment of malware codes for embedded platforms. In addition to it, we also present a security analysis to show the difficulty of a mimicry attack attempting to bypass LAMBDA.