Yuling Chen

Severin Beger, Yuling Chen, Sandra Hirche

Ensuring safe behavior is critical for modern autonomous cyber-physical systems. Control barrier functions (CBFs) are widely used to enforce safety in autonomous systems, yet their placement within networked control architectures remains largely unexplored. In this work, we investigate where to enforce safety in a networked control system in which a remote model predictive controller (MPC) communicates with the plant over a delayed network. We compare two safety strategies: i) a local myopic CBF filter applied at the plant and ii) predictive CBF constraints embedded in the remote MPC. For both architectures, we derive state-dependent disturbance tolerance bounds and show that safety placement induces a fundamental trade-off: local CBFs provide higher disturbance tolerance due to access to fresh state measurements, whereas MPC-CBF enables improved performance through anticipatory behavior, but yields stricter admissible disturbance levels. Motivated by this insight, we propose a combined architecture that integrates predictive and local safety mechanisms. The theoretical findings are illustrated in simulations on a planar three-degree-of-freedom robot performing a collision-avoidance task.

Yuling Chen, Juan Ma, Xianmin Wang et al.

Rational secure multi-party computation (RSMC) means two or more rational parties to complete a function on private inputs. In the process, the rational parties choose strategies to maximize utility, which will cause players to maliciously execute the protocol and undermine the fairness and correctness of the protocol. To solve this problem, we leverage game theory to propose the direction entropy-based solution. First, we utilize the direction vector of the direction entropy to examine the player's strategy uncertainty and quantify its strategy from different dimensions. Specifically, when parties choose a cooperation strategy, the direction vector is positive, and the information transmitted is positive, conversely, it is negative information. Then, we provide mutual information to construct new utility functions for the players. What's more, we measure the mutual information of players to appraise their strategies. Finally, we prove in detail the protocol we gave, and the result show that the fairness problem in rational secure two-party computation. We also prove that the proposed protocol reaches the Nash equilibrium. Furthermore, we conduct experiments using mutual information to construct utility, and the results show that the utility obtained when the player is honest will be higher.