  Existing large-scale optimization schemes are challenged by both scalability
and cyber-security. With the favorable scalability, adaptability, and
flexibility, decentralized and distributed optimization paradigms are widely
adopted in cyber-physical system applications. However, most existing
approaches heavily rely on explicit information exchange between agents or
between agents and the system operator, leading the entire framework prone to
privacy risks. To tackle this issue, this paper synthesizes cryptography and
decentralized optimization techniques to develop a novel privacy-preserving
decentralized optimization paradigm. The proposed paradigm is generically
applicable to strongly coupled convex optimization problems with nonseparable
objective functions and linearly coupled constraints. The security and accuracy
of the proposed paradigm are verified via numerical examples.
