## 2 Related Work  
Many works have focused on offloading computations to fog or edge devices in order to address latency and energy consumption. [PERSON] et al. [29] and [PERSON] et al. [30] address latency optimized and energy optimized offloading respectively. [PERSON] et al. [31] optimizes the computational energy efficiency and latency on offloading to the mobile edge. [PERSON] et al. [20] and [PERSON] et al. [19] discuss multi-user task offloading, which involves multiple users offloading tasks to an edge server. Partial offloading which involves both local processing and edge processing to reduce latency and energy have been discussed by [PERSON] et al. [32] and [PERSON] et al. [33] respectively. Cooperative offloading, i.e., offloading computation tasks with the help of relay nodes to MEC servers far from the users to meet the computational demands, have been discussed in Reference [34]. Task offloading with energy-harvesting have been discussed by [PERSON] et al. [35] and [PERSON] et al. [36]. Extensive literature on this work can be found in References [21, 37]. However, these works does not address the privacy or security issues faced by the users and the user devices. Privacy (especially location-based privacy) have been explored in various networking environments such as wireless sensor networks [38], wireless networks [39], and location-based services [40]. Data privacy has been studied extensively leading the development of several new encryption techniques and distributed multi-party computational solutions; see for example, References [41; 42; 43]. Large number of works have discussed the privacy and security issues while offloading computations in an MEC network (see the surveys in References [17; 18]). However, most of these works address privacy and security issues derived from older cloud based systems. There are only a few works that address privacy and security issues specific to edge and fog networks. He et al. [44] have proposed Constrained Markov Decision Process (CMDP) based technique for offloading computations to the mobile edge to reduce energy consumption and delay with a goal to maintain a pre-specified level of privacy (location and usage pattern). Preserving location privacy while optimizing the energy consumption in presence of a compromised edge service provider have been discussed in Reference [45], where a deep post decision state (PDS) learning algorithm that exploits information about energy harvesting process, has been proposed to offload to various edge devices so that the energy consumption is minimal while maintaining specified privacy. [PERSON] et al. [46] propose privacy-preserving and cost-efficient (PEACE) task offloading scheme that can preserve user location privacy when there is a user presence inference attack that invades user privacy when offloading tasks to the edge nodes. PEACE is developed based on Lyapunov optimization framework. These works have mainly focussed on offloading with privacy without considering security and other design challenges. Even though physical layer security techniques such as noisy signals and receiver jamming ([47]) have been utilized to promote the security and privacy of the conventional wireless communication systems [48], there are very few recent works that utilize such techniques. A novel secure offloading scheme based on physical layer has been proposed in Reference [49]. In this work, the edge server broadcasts jamming signals and utilizes full duplex communication to impede the eavesdroppers and reduce the self interference. The optimal jamming signal and offloading ratio is solved as a bilevel optimization problem. Efficient offloading algorithm is developed based on this technique. Similarly, [PERSON] et al. [50] utilize a physical layer based technique and massive multiple input multiple output (mIMO) to provide security while the user's total energy consumption is minimized by jointly optimizing the user's offloading data bits, transmission power, and offloading rate.