The emergence of video streams as a primary medium for communication and the demand for high-quality video sharing over the internet have given rise to several security and privacy issues, such as unauthorized access and data breaches. To address these limitations, various Selective Video Encryption (SVE) schemes have been proposed, which encrypt specific portions of a video while leaving others unencrypted. The SVE approach balances security and usability, granting unauthorized users access to certain parts while encrypting sensitive content. However, existing SVE schemes adopt an all-or-nothing coarse-grain encryption approach, where a user with a decryption key can access all the contents of a given video stream. This paper proposes and designs a fine-grained access control-based selective video encryption scheme, ABSVE, and a use-case protocol called Blind Brother. Our scheme encrypts different identified Regions of Interest (ROI) with a unique symmetric key and applies a Ciphertext Policy Attribute Based Encryption (CP-ABE) scheme to tie these keys to specific access policies. This method provides multiple access levels for a single encrypted video stream. Crucially, we provide a formal syntax and security definitions for ABSVE, allowing for rigorous security analysis of this and similar schemes - which is absent in prior works. Finally, we provide an implementation and evaluation of our protocol in the Kvazaar HEVC encoder. Overall, our constructions enhance security and privacy while allowing controlled access to video content and achieve comparable efficiency to compression without encryption.

Machine Learning (ML) has become one of the most impactful fields of data science in recent years. However, a significant concern with ML is its privacy risks due to rising attacks against ML models. Privacy-Preserving Machine Learning (PPML) methods have been proposed to mitigate the privacy and security risks of ML models. A popular approach to achieving PPML uses Homomorphic Encryption (HE). However, the highly publicized inefficiencies of HE make it unsuitable for highly scalable scenarios with resource-constrained devices. Hence, Hybrid Homomorphic Encryption (HHE) – a modern encryption scheme that combines symmetric cryptography with HE – has recently been introduced to overcome these challenges. HHE potentially provides a foundation to build new efficient and privacy-preserving services that transfer expensive HE operations to the cloud. This work introduces HHE to the ML field by proposing resource-friendly PPML protocols for edge devices. More precisely, we utilize HHE as the primary building block of our PPML protocols. We assess the performance of our protocols by first extensively evaluating each party’s communication and computational cost on a dummy dataset and show the efficiency of our protocols by comparing them with similar protocols implemented using plain BFV. Subsequently, we demonstrate the real-world applicability of our construction by building an actual PPML application that uses HHE as its foundation to classify heart disease based on sensitive ECG data.

Functional Encryption (FE) is a cutting-edge cryptographic technique that enables a user with a specific functional decryption key to determine a certain function of encrypted data without gaining access to the underlying data. Given its potential and the fact that FE is still a relatively new field, we set out to investigate how it could be applied to resource-constrained environments. This work presents what we believe to be the first lightweight FE scheme explicitly designed for resource-constrained devices. We also propose a use case protocol that demonstrates how our scheme can secure an Internet of Things (IoT) architecture where relevant devices collect data and securely deliver them to a storage server, where an analyst can request access to the encrypted data. Finally, we conduct thorough experiments on two commercially available resource-constrained devices to provide compelling evidence of our approach’s practicality and efficiency. Although the results of our evaluations show that there is room for improvement in the proposed scheme, this work represents one of the first attempts to apply FE to the IoT setting that can directly impact people’s daily lives and the everyday operations of organizations.

The proliferating adoption of the Internet of Things (IoT) paradigm has fuelled the need for more efficient and resilient access control solutions that aim to prevent unauthorized resource access. The majority of existing works in this field follow either a centralized approach (i.e. cloud-based) or an architecture where the IoT devices are responsible for all decision-making functions. Furthermore, the resource-constrained nature of most IoT devices make securing the communication between these devices and the cloud using standard cryptographic solutions difficult. In this paper, we propose a distributed access control architecture where the core components are distributed between fog nodes and the cloud. To facilitate secure communication, our architecture utilizes a Certificateless Hybrid Signcryption scheme without pairing. We prove the effectiveness of our approach by providing a comparative analysis of its performance in comparison to the commonly used cloud-based centralized architectures. Our implementation uses Azure – an existing commercial platform, and Keycloak – an open-source platform, to demonstrate the real-world applicability. Additionally, we measure the performance of the adopted encryption scheme on two types of resource-constrained devices to further emphasize the applicability of the proposed architecture. Finally, the experimental results are coupled with a theoretical analysis that proves the security of our approach.

Even though the idea of transforming basic objects to smart objects with the aid sensors is not new, it is only now that we have started seeing the incredible impact of this digital transformation in our societies. There is no doubt that the Internet of Things (IoT) has the power to change our world and drive us to a complete social evolution. This is something that has been well understood by the research and industrial communities that have been investing significant resources in the field of IoT. In business and industry, there are thousands of IoT use cases and real-life IoT deployments across a variety of sectors (e.g. industry 4.0 and smart factories, smart cities, etc.). However, due to the vastly resource-constrained nature of the devices used in IoT, implementing secure and privacy-preserving services, using, for example, standard asymmetric cryptographic algorithms, has been a real challenge. The majority of IoT devices on the market currently employ the use of various forms of symmetric cryptography such as key pre-distribution. The overall efficiency of such implementations correlates directly to the size of the IoT environment and the deployment method. In this paper, we implement a lightweight cryptographic library that can be used to secure communication protocols between multiple communicating nodes without the need for external trusted entities or a server. Our work focuses on extending the functionalities of the User Datagram Protocol (UDP) broadcast application on the Contiki-NG Operating System (OS) platform.