Conference Agenda

The growing number of Internet of Things (IoT) devices necessitates the use of reliable and efficient cryptographic solutions to ensure the safety of data transmission and storage. Traditional cryptographic algorithms can be difficult to implement without sacrificing performance or security due to significant resource constraints in many IoT devices. This paper aims to evaluate the performance of classical and post-quantum cryptographic libraries on IoT hardware, specifically using PyCryptodome Python Library for traditional cryptography and Kyber 512 for post-quantum system for encryption. In resource-constrained environments, the study evaluates the security features, computational efficiency, and feasibility of these libraries. The Raspberry Pi 5 platform was used to conduct experiments that analyzed encryption and decryption performance, giving insights into the trade-offs between security and processing overhead. Using the findings, one can choose the best cryptographic solutions for IoT devices based on their performance, security, and hardware limitations.

Using the Kyber 512 algorithm on a Raspberry Pi platform, a practical implementation of post-quantum cryptographic algorithms was carried out as part of this study. Python was used to analyze the feasibility of deploying quantum-resistant cryptography in IoT environments through this implementation. Cryptographic performance in constrained environments is compared scientifically through the measurement of encryption and decryption times in the study. The research provides valuable insight into the computational overhead and feasibility of implementing post-quantum cryptographic solutions on hardware that is limited in resources. Scientific graphs were created to illustrate the complexity of encryption and decryption time, providing a visual representation of performance variations in various scenarios. The trade-offs between security and computational efficiency, which are crucial for real-world IoT applications, can be understood by understanding these graphs. In addition, the study discusses potential optimizations and future directions that can enhance the efficiency of cryptographic operations in IoT hardware. To sum up, this paper advances the study of cryptographic applications for IoT by exploring the viability of post-quantum cryptographic algorithms in constrained environments. By considering the findings, IoT security can be strengthened against emerging cyber threats by selecting optimized cryptographic libraries that balance security, performance, and hardware limitations.

Quantum computing threatens current cryptographic methods, with the capability to break widely used algorithms like RSA and ECC, which secure today's digital infrastructure (Chen et al., 2025). As these threats grow, exploring Post-Quantum Cryptography (PQC), i.e. cryptographic algorithms designed to be secure against the potential threats posed by quantum computers, becomes urgent to secure smart city data and services. Implementing Post-Quantum Cryptography in smart city systems is vital to counter future quantum threats while also ensuring compatibility with existing technologies, especially resource-constrained IoT devices.

Recognizing the threat quantum computing poses to classical cryptography, the National Institute of Standards and Technology (NIST) initiated a standardization process to identify and evaluate PQC algorithms. We implemented a “PQC-Smart-City-Data” benchmarking system based on i) the PQClean library (Kannwischer et al., 2022), a C-based library that provides implementations of all PQC algorithms currently considered by NIST, and ii) on a Rust-based library that provides an implementation of the Kyber Post-Quantum key encapsulation mechanism (Argyle-Software, 2025). Our testing system consist of two Raspberry Pi devices (Pi3 and Pi2W), which each can act as either a client or as a server in a particular experiment. The data being sent cryptographically protected from client to server consists of 100 real-world temperature measurements from actual IoT sensors, encoded in JSON format. Our experimental results show the time needed for each individual cryptographic operation at the client and at the server, respectively.

Mobility data has become a strategic asset in urban planning, crisis management and smart city operations. However, centralized systems for mobility tracking raise severe privacy concerns (De Montjoye et al., 2013), as they often lack transparency, enforce global data collection and have the ability to directly link individuals to their movements. To address these issues, we introduce LoChain, a decentralized protocol that enables the privacy-preserving collection and processing of mobility data based on blockchain technology. LoChain is designed to be decentralized, minimize data collection, reduce the risk of reidentification while promoting regional autonomy over mobility information.

When recording street scenes, the privacy of the people concerned must always be taken into account. Previous work has focused heavily on directly identifiable features such as faces or license plates. However, the visibility of indirectly identifiable objects can also limit the protection of citizens. This is especially true for cars. Regular recordings could be used to infer when a person is at home or where else they have been.
The obvious solution would be to simply make cars unrecognizable. However, previous work has shown that this has a negative impact on downstream tasks such as traffic sign recognition. Therefore, in this work, we use generative models to synthetically modify cars. We compare models from the generative adversarial network (DP-GAN, OASIS) and diffusion model communities (Kolors, SDXL) to see which ones are best suited in terms of anonymity, image integrity, and performance.
For the GAN-based models, we use an image-to-image translation approach to modify only image sections with cars, while for the diffusion models, we develop two methods that use text-guided image inpainting. We compare the developed methods using established metrics and perform a survey with test subjects.
In terms of anonymity, all models achieve convincing results, while diffusion models that generate each car mask individually produce particularly realistic images. In return, GAN-based methods process images more than twice as fast creating a trade-off between image integrity and performance.