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Although massive real-time data collected from users can provide benefits to improve the quality of human daily lives, it is possible to expose users' privacy. -differential privacy is a notable model to provide strong privacy preserving in statistics. The existing works highlight ω-event differential privacy with a fixed window size, which may not be suitable for many practical scenarios. In view of this issue, we explore a real-time scheme with adaptive ω-event for differentially private time-series publishing (ADP) in this paper. In specific, we define a novel notion, Quality of Privacy (QoP) to measure both the utility of the released statistics and the performance of privacy preserving. According to this, we present an adaptive ω-event differential privacy model that can provide privacy protection with higher accuracy and better privacy protection effect. In addition, we also design a smart grouping mechanism to improve the grouping performance, and then improve the availability of publishing statistics. Finally, comparing with the existing schemes, we exploit real-world and synthetic datasets to conduct several experiments to demonstrate the superior performance of the ADP scheme.1. Introduction. Recently, context-aware smart devices are experiencing an unprecedented surge in smart cyber-physical networks [39]. These devices can achieve ubiquitous connectivity and powerful raw data-gathering capabilities via humancentric and device-to-device (D2D)-based applications [31]. Using data mining technologies, we then can discover knowledge from huge amounts of raw data. Accordingly, we learn that the technology may provides great benefits to push against our cognition of human behavior and to improve the quality of our lives.