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Location-aware applications have been used widely with the assistance of the latest positioning features in Smart Phone such as GPS, AGPS, etc. However, all the existing applications gather users’ geographical data and transfer them into the pertinent information to give meaning and value. For this kind of solutions, the user’s privacy and security issues might be raised because the geographical location has to be exposed to the service provider. A novel and practical solution is proposed in this article to provide the relative location of two mobile users based on their WiFi scanned results without any additional sensors. There is no privacy concern in this solution because end users will not collect and send any sensitive information to the server. This solution adopts a Client/Server (C/S) architecture, where the mobile user as a client reports the ambient WiFi APs and the server calculates the distances based on the WiFi AP’s topological relationships. A series of technologies are explored to improve the accuracy of the estimated distance and the corresponding algorithms are proposed. We also prove the feasibility with the prototype of “Circle Your Friends” System (CYFS) on Android phone which lets the mobile user know the distance between him and his social network friends.




Location awareness refers to devices that can passively or actively determine their location. Navigational instruments provide location coordinates for vessels and vehicles. Surveying equipment identifies location with respect to a well-known location a wireless communications device. Network location awareness (NLA) describes the location of a node in a network. The term applies to navigating, real-time locating and positioning support with global, regional or local scope. The term has been applied to traffic, logistics, business administration and leisure applications. Location awareness is supported by navigation systems, positioning systems and/or locating services. Location awareness without the active participation of the device is known as non-cooperative locating or detection. Location-aware applications use the geographical position of a mobile worker or an asset to execute a task. Position is detected mainly through satellite technologies, such as a GPS, or through mobile location technologies in cellular networks and mobile devices.

Examples include fleet management applications with mapping, navigation and routing functionalities, government inspections and integration with geographic information system applications.  Location-aware applications deliver specified messages to users based on their physical location. This kind of services can be divided into two types: absolute-location services and relative-location services. Absolute location is locating a place using a coordinate system while relative location means to locate a place relative to other landmarks. Location services require the users to report their absolute location data to the server and then the server return the querying result. Usually the technologies to detect and retrieve the location data include GPS, mobile cell id (CID), WiFi AP. For these methodologies, serious privacy concerns are raised because they enable the continuous tracking of involved users’ location. Two major types of privacy concerns are triggered: the potential information leakage in communications and the inappropriate usage of this information by the service providers.


The rapid proliferation of smart phone technology in urban communities has enabled mobile users to utilize context aware services on their devices. Service providers take advantage of this dynamic and ever-growing technology landscape by proposing innovative context-dependent services for mobile subscribers. Location-based Services (LBS), for example, are used by millions of mobile subscribers every day to obtain location-specific information .Two popular features of location-based services are location check-ins and location sharing. By checking into a location, users can share their current location with family and friends or obtain location-specific services from third-party providers, the obtained service does not depend on the locations of other users.

The other types of location-based services, which rely on sharing of locations (or location preferences) by a group of users in order to obtain some service for the whole group, are also becoming popular. According to a recent study, location sharing services are used by almost 20% of all mobile phone users. One prominent example of such a service is the taxi-sharing application, offered by a global telecom operator, where smart phone users can share a taxi with other users at a suitable location by revealing their departure and destination locations. Similarly, another popular service enables a group of users to find the most geographically convenient place to meet.


  • Privacy of a user’s location or location preferences, with respect to other users and the third-party service provider, is a critical concern in such location-sharing-based applications.
  • For instance, such information can be used to de-anonymize users and their availabilities, to track their preferences or to identify their social networks.
  • For example, in the taxi-sharing application, a curious third-party service provider could easily deduce home/work location pairs of users who regularly use their service.
  • Without effective protection, evens parse location information has been shown to provide reliable information about a users’ private sphere, which could have severe consequences on the users’ social, financial and private life.
  • Even service providers who legitimately track users’ location information in order to improve the offered service can inadvertently harm users’ privacy, if the collected data is leaked in an unauthorized fashion or improperly shared with corporate partners.


 We propose a simple and novel solution to provide the relative distance of two mobile devices without collecting any personal sensitive data. It can guarantee 100% privacy of the users when providing location based service. Since no absolute information is detected, none of above privacy-protected mechanisms needs to be adopted in our solution. At the same time, some methods are put forward to improve the accuracy of the relative distance. Our approach undertakes and integrates more parameters to improve the accuracy for WiFi positioning system such as IEEE protocol type, overlap ratio, etc. More importantly, all these mechanisms have been revisited and redesigned carefully to make them more applicable.

We address the privacy issue in LSBSs by focusing on a specific problem called the CYFS. Given a set of user location preferences, the CYFS is to determine a location among the proposed ones such that the maximum distance between this location and all other users’ locations is minimized, i.e. it is fair to all users. To prove its feasibility, a prototype based on Facebook is developed on Android based mobile devices. By evaluating the accuracy of estimated distance, though the precision is not good as GPS, it has proved that our privacy-free solution is suitable for social networking and location-based application. The future work includes developing the application on Google Android as well as Apple IOS devices. Furthermore, if possible, it also includes integrating the privacy-preserving relative location based service into other social networking applications such as Wechat and QQ.


  • In the proposed system, Problem in a privacy-preserving fashion, where each user participates by providing only a single location preference to the CYFS solver or the service provider.
  • In this significantly extended version of our earlier conference paper, we evaluate the security of our proposal under various passive and active adversarial scenarios, including collusion.
  • We also provide an accurate and detailed analysis of the privacy properties of our proposal and show that our algorithms do not provide any probabilistic advantage to a passive adversary in correctly guessing the preferred location of any participant.
  • In addition to the theoretical analysis, we also evaluate the practical efficiency and performance of the proposed algorithms by means of a prototype implementation on a test bed of Nokia mobile devices. We also address the multi-preference case, where each user may have multiple prioritized location preferences.
  • We highlight the main differences, in terms of   performance, with the single preference case, and also present initial experimental results for the multi-preference implementation. Finally, by means of a targeted user study, we provide insight into the usability of our proposed solutions.



v    Processor                                 –    Pentium –IV

  • Speed       –    1 GHz
  • RAM       –    256 MB (min)
  • Hard Disk      –   20 GB
  • Floppy Drive       –    44 MB
  • Key Board      –    Standard Windows Keyboard
  • Mouse       –    Two or Three Button Mouse
  • Monitor                            –    SVGA


  • Operating System        :           Windows XP or Win7
  • Front End       :           JAVA JDK 1.7
  • Back End :           MYSQL Server
  • Script :           JSP Script       
  • Document                               :           MS-Office 2007