In many multi-agent Cyber-Physical Systems (CPS), the agents are self-interested in that their individual costs/rewards are not fully aligned with the network-level global objective function. A typical example is the large-scale coordinated charging of electric vehicles, where the network-level goal usually focuses on filling the overnight decrease in background power demand while individual electric vehicles only mind their own charging costs. Therefore, an opportunistic self-interested agent may be tempted to lie in information sharing to reduce its local cost. Motivated by the observation that privacy and truthfulness are closely intertwined, the project aims to investigate how to ensure both privacy and truthfulness in self-interested multi-agent CPS. The project?s novelties include the simultaneous treatment of both privacy and truthfulness in multi-agent CPS and the development of a novel framework and technical approaches for privacy-preserving and truthfulness-guaranteed multi-agent CPS. The project's impacts are closely aligned with societal goals in efficient, privacy-preserving, and truthfulness-guaranteed self-interested multi-agent CPS, and will set up the stage for achieving the goal of improved efficiency and effectiveness in large-scale CPS in sectors like energy and transportation.<br/><br/>The project aims to address the following five tasks: 1) Leveraging the investigators? prior results on distributed unconstraint optimization that achieve differential privacy by compromising convergence speed rather than convergence accuracy, ensure both differential privacy and accurate convergence in multi-agent optimization subject to shared inequality constraints; 2) Exploiting the fact that differential privacy can be used as a tool for truthfulness, ensure truthful behaviors of agents participating in distributed optimization; 3) Leveraging the investigators? prior results on constraint-free Nash equilibrium seeking that achieve differential privacy by compromising convergence speed rather than convergence accuracy, ensure both differential privacy and accurate convergence in generalized Nash equilibrium seeking subject to shared inequality constraints; 4) Exploiting the fact that differential privacy can be used as a tool for truthfulness, ensure truthful behaviors of agents in generalized Nash equilibrium seeking in the partial-decision information setting; 5) Enhance the differential-privacy based approximate truthfulness to establish exact truthfulness in multi-agent optimization and Nash equilibrium seeking. The proposed algorithms and frameworks will be evaluated using both numerical simulations and experiments of coordinated charging of real electric vehicles in collaboration with Ford Motor Company. The teams is using existing undergraduate research programs and various on-going outreach opportunities to energize interests in STEM in minority middle-school girls (Clemson WISE program), high-school students (MSU HSEI program), and community-college technicians (TriCounty Technical College).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.