Due to the rapid proliferation of Unmanned Aerial Vehicles (UAVs), also termed as drones, the Internet of Drones (IoD) has revolutionized various domains including disaster response, smart surveillance, remote sensing, by facilitating real time collection of aerial data using autonomous coordination. However, the exposed communication landscape of IoD networks render them highly susceptible to known threats including forgery, impersonation and physical capture threats. Most of the conventional key agreement techniques are costly for computations and not suitable for resource constrained IoD system which necessitate low cost yet secure authentication mechanisms. Though, many lightweight drone authentication schemes have been presented, however with security limitations. This study suggested a novel Barrel-Shifter Physically Unclonable Function (BS-PUF) based drone authentication protocol (BSP-IoD) to augment security of IoD network. Leveraging the intrinsic randomness, commutative and invertible properties of BS-PUF, the BSP-IoD ensures construction of mutually agreed session key employing unique challenge-response pairs (CRPs). The scheme could withstand known threats besides supporting perfect forward secrecy and privacy to the user. The BSP-IoD considerably mitigates computational overheads besides ensuring resilience against known attacks including resistance from drone physical capture threat, promoting viability for next generation IoD networks. The formal analysis and performance assessment exhibit that BSP-IoD could address the critical challenges of next generation IoD applications. Furthermore, it supports 56.6% more number of security properties as compared to preceding schemes.

Abstract Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications.

In a smart health system, patients' location information is periodically sent to hospitals and this information helps hospitals to provide improved health care services. The location information together with time stamp alone can reveal a patient's private information, such as person's life style, places frequently visited by the person, and personal interests. Thus, it is important to protect the location privacy of a patient. In the existing privacy protection mechanisms, trusted third party (TTP) and location perturbation techniques are used. However, in TTP-based mechanism, an adversary who illegally gets access to TTP server will have access to the private location information. On the other hand, in location perturbation technique, utility of the location information is significantly compromised. In this paper, we propose a location privacy protection mechanism in which location privacy is protected while maintaining the utility of the location data. In the proposed mechanism, a main processing unit attached to a patient's body generates the perturbed location by considering the distance between the patient's location and the preidentified patient's sensitive locations. This adaptive generation of perturbed location, removes the necessity to trust other parties while preserving the privacy and utility of the location data. The validity of the proposed mechanism is demonstrated by simulation results.