# Trustworthiness in Mobile Cyber-Physical Systems
## 2 Review of Issue Contents  
This Special Issue presents nine original papers covering the latest advances and technologies involved in the design of reliable, resilient, secure, and intelligent MCPSs. Moreover, each paper contributes research that offers insights regarding trustworthiness in MCPSs. Artificial intelligence models, especially deep neural networks such as convolutional neural nets (CNNs), tend to have many learning parameters, thus making their integration into small embedded CPSs, such as mobile phones, challenging. In response to this issue, [PERSON] et al. in [2] suggested a new model compression framework based on sparse coding and knowledge distillation with adversarial training, thereby producing compact CNN architectures that maintain robustness against adversarial perturbed inputs. Furthermore, the authors provide training algorithms based on the alternating direction method of multipliers (ADMM), which is more memory-efficient than existing CNN pruning methods and, therefore, more suitable for AI-enabled MCPSs. In [3], [PERSON] et al. propose two novel data quality measures suitable for large-scale high-dimensional data. As low-quality data can degrade prediction accuracy and inference bias, measuring the data quality is an important first step in successful AI applications. In MCPS, the use of AI often requires regular updates, while detecting inference bias when operating at the AI runtime is difficult; therefore, a data quality check is essential. This study also proposes efficient algorithms based on random projections and bootstrapping, enabling the suggested measures to be computed for large-scale and high-dimensional data, thus representing a departure from existing data quality measures. Automotive systems are typical examples of CPSs in which embedded software is the main element controlling the mechanical components of the vehicle. Internet-connected software components can be victims of security attacks at any time, and CAN (Controller Area Networks), an in-vehicle network system connecting individual electronic control units (ECUs), serves as a breach point to break vehicle safety. MAuth-CAN [4] is a new CAN authentication technique that protects ECUs from attacking messages based on a centralized node called an authenticator. It is secure against masquerade attacks by a compromised node and protects the authenticator node from bus-off attacks that can temporarily force an ECU to leave CAN. However, the use of a central node causes an additional authentication delay. Thus, in accordance with regulations such as ISO 26262, the efficacy of the MAuth-CAN must be formally verified before it can be used for commercial vehicles. [PERSON] et al. [5] present formal proof that MAuth-CAN is consistently resistant against message flooding and Bus-Off attacks and provide formal CAN models at various levels, which can be used to analyze CAN applications. Via model checking, the complicated behavior of CAN in the media access control level of the data link layer connecting to MAauth-CAN was checked exhaustively to prove its resilience and sustainability under such attacks. These results can be used to obtain safety certificates from regulatory authorities, while the methodology and the CAN models can be used to secure safety certificates regarding CAN applications. Public key encryption with keyword search (PEKS) functionality enables users to search for encrypted data that has been outsourced to an untrusted server. Unfortunately, updates to the outsourced data may cause information leakage by exploiting the queries previously submitted in PEKS. [PERSON] et al. [6] address this by proposing a novel forward private PEKS scheme based on software guard extension (SGX), a trusted execution environment provided by Intel. By utilizing SGX, the proposed scheme presents substantial performance improvements compared with prior work.