# An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services
## 1 Introduction  
In recent years, with the rapid development of smart mobile devices and mobile communication technologies, Internet of Things (IoT) services have emerged in our daily life [1]. According to the annual report of the International Data Corporation, the IoT market will exceed $7 trillion by 2020 [2]. Many potential IoT applications rely on location-based services (LBS) or LBS applications, such as GPS navigators, information retrieval, and context-aware mobile applications, etc. [3]. To benefit from these IoT applications, users can obtain the required geographic location services by sending their current location or point of Interest (POI) to the untrusted LBS providers (LSPs), for instance, querying to find the nearest shopping center [4]. Although LBS provides enormous convenience and benefits to IoT users, it also raises serious privacy concerns because location information is collected by untrusted or malicious LSPs [4]. By analyzing users' location information, untrusted or malicious LSPs can infer users' personal information by associating a user's identity with queried locations and interests [4], such as their home addresses, sexual preference and health conditions, etc. In order to alleviate the location privacyconcerns in IoT services and applications, many privacy protection schemes have been proposed recently. Most of the schemes are based on location k-anonymity technology. The k-anonymity technique is one of the most important techniques to protect users' location privacy in LBS; it can ensure that a user's location is identified with a probability at less than or equal to 1/k. The dummy location is a fake query location that is similar enough to the user's location so that the attacker can not distinguish which is the user's real location from the anonymous set. Dummy locations are widely used to realize location k-anonymity due to the following advantages: (1) they do not rely on complex encryption and decryption schemes, (2) they effectively save computing resources of the IoT device, (3) they achieve better trade-offs between LBS privacy and utility. The dummy-based location privacy-preserving (DLP) scheme is widely used in IoT services [5; 6; 7; 8; 9; 10], which focus on how to effectively select reasonable dummy locations and avoid having the real location distinguished by LSPs. However, these schemes do not fully consider the side information [5; 7; 10]. In reality, some attackers may have such side information, and these dummy-based location selection schemes are inadequate to preserve IoT users' privacy. Some schemes do consider the side information available to attackers [6; 8; 9], and these schemes are based on the entropy metric [11]. However, the computational cost of these schemes is very expensive, and this is not suitable for resource-constrained IoT devices. To the best of our knowledge, how to select reasonable dummy locations is still a challenge, especially for a data-driven IoT service. In this paper, we propose an enhanced dummy-based location privacy-preserving scheme (Enhanced-DLP), which aims for practical efficiency for IoT users and devices with strong location privacy protection. The basic idea of Enhanced-DLP is that the dummy locations are selected to build an optimal anonymous set for IoT user by considering the side information that is available to an adversary. Specifically, the Enhanced-DLP scheme adopts an enhanced greedy scheme when constructing the anonymous set. The dummy location that is most similar with the current anonymous set will be added to the anonymous set. Comparing our Enhanced-DLP with previous state-of-the-art schemes, the experimental results illustrate that our Enhanced-DLP scheme can reduce the computational costs while preventing location privacy breaches from adversaries with side information. The main contributions of this paper are as follows:  
1. The dummy-based privacy protection scheme is a privacy protection scheme without a third trusted party, and is more suitable for the IoT environment.