# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 1 Introduction  
Location-Based Services (LBSs) have become an integral part of modern life, offering a wide range of functionalities that enhance the convenience, efficiency, and personalization of everyday tasks. From outdoor navigation for driving and walking, to real-time tracking of assets and people, LBSs are crucial in industries such as logistics, transportation, tourism, and healthcare. These services help businesses optimize operations, improve customer experiences, and enable new business models, such as location-based advertising. With the widespread adoption of smartphones and connected devices, LBSs are now deeply embedded in how people interact with their environment, whether it is finding the nearest restaurant, sharing location with friends, or getting directions in an unfamiliar area. While Global Navigation Satellite Systems (GNSSs) like GPS or Galileo have made outdoor localization ubiquitous, their effectiveness diminishes significantly in indoor environments, where obstacles such as walls and ceilings disrupt signal reception. This limitation has spurred the development of various indoor localization (IL) techniques [1], which leverage technologies such as WiFi [2, 3], Bluetooth [4], RFID [5], and cellular networks [6]. Among these, WiFi fingerprinting has emerged as a widely adopted solution due to the widespread deployment of WiFi infrastructure and the ability of mobile devices to measure WiFi signal strengths. WiFi-based localization relies on measuring the received signal strengths (RSSs) from multiple WiFi access points (APs) within the environment [7, 8]. The key idea is to use the strength of the WiFi signals received from various APs, which vary depending on the distance between the user and the APs. These RSS values are then compared to a pre-established database known as a \"fingerprint\" database. This database contains thesignal strengths of different APs measured at known locations called reference points (RPs) in the area. By matching the current RSSs with those in the database, the user's position can be estimated accurately. This method is particularly useful in indoor environments where GPS signals are unavailable or unreliable. Despite the advancements in IL techniques, privacy concerns remain a major challenge. Most IL methods require users to send their real-time location-dependent measurements (e.g., WiFi RSSs) to a server, where localization is performed. This setup poses significant privacy risks: (1) user privacy leakage: the server can track the user's movement patterns inside the building; (2) service provider control: users have limited control over how their location data is stored and processed. Moreover, location data is highly sensitive and can be exploited to infer users' identities, habits, and even predict their future movements. Given the increasing regulatory requirements (e.g., GDPR [9]), there is a strong need for privacy-preserving indoor localization solutions. To mitigate privacy risks, privacy-preserving indoor localization (PPIL) techniques have been proposed [10; 11; 12; 13; 14]. These solutions aim to prevent unauthorized tracking while still enabling accurate indoor localization. However, existing PPIL methods face significant challenges that limit their practical adoption: High computational overhead leads to excessive processing time, making real-time localization infeasible [10; 11; 12; 15]. Excessive communication costs result in network inefficiencies and scalability issues, especially in large-scale or dense deployments [13; 14; 16]. Trade-offs between security and performance often undermine the privacy guarantees, resulting in schemes that may be vulnerable or unreliable [17]. These limitations--high computation, heavy communication, and weakened security--collectively hinder the practical deployment and effectiveness of current PPIL solutions. This motivates us to design a more efficient secure indoor localization scheme while ensuring the privacy of both users and the data owner.