A　new　trend　of　using　deep　reinforcement　learning　for　traffic　signal　control　has　become　a　spotlight　in　the　Intelligent　Transportation　System　(ITS).　However,　the　traditional　intelligent　traffic　signal　control　system　always　collects　and　transmits　vehicle　information　(e.g.,　vehicle　location,　speed,　etc.)　in　the　form　of　plaintext,　which　would　result　in　the　leakage　of　commuters＇　privacy　and　thus　bring　unnecessary　troubles　to　users.　In　this　paper,　we　propose　a　privacy-preserving　traffic　signal　control　for　an　intelligent　transportation　system　(PrivacySignal).　It　relies　on　the　existing　road　facilities　to　achieve　the　privacy　of　commuters,　which　guarantees　the　practicality　of　the　system.　Real-time　decision-making　and　confidentiality　of　the　system　can　be　achieved　simultaneously　via　the　design　of　a　series　of　secure　and　efficient　interactive　protocols,　that　are　based　on　additive　secret　sharing,　to　perform　the　deep　Q-network　(DQN).　Moreover,　the　security　of　PrivacySignal　is　testified,　meanwhile,　the　system　effectiveness,　and　the　overall　efficiency　of　PrivacySignal　is　demonstrated　through　theoretical　analysis　and　simulation　experiments.　Compared　with　the　existing　privacy-preserving　schemes　of　the　intelligent　traffic　signal,　PrivacySignal　provides　a　general　DQN　based　privacy-preserving　traffic　signal　control　strategy　architecture　with　high　efficiency　and　low-performance　loss.