Currently, edge computing (EC), emerging as a burgeoning paradigm, is powerful in handling real-time resource provision for Internet of Things (IoT) applications. However, due to the spatial distribution of geographically sparse IoT devices and the resource limitations of EC units (ECUs), the resource utilization of corresponding edge servers is relatively insufficient and the execution performance is ineffective to some extent. A privacy leakage, including personal information, location, media data, etc., during the transmission process from IoT devices to edge servers severely restricts the application of ECUs in IoT. To address these challenges, a two-phase offloading optimization strategy is put forward for joint optimization of offloading utility and privacy in EC enabled IoT. Technically, a utility-aware task offloading method, named UTO, is devised first to obtain the goal of maximizing the resource utilization of ECUs and minimizing the implementation time cost. Then a joint optimization method, named JOM, for utility and privacy tradeoffs is designed to balance the privacy preservation and execution performance. Eventually, the experimental evaluations are designed to illustrate the efficiency and reliability of UTO and JOM.