User-Defined Privacy Grid System for Continuous Location-Based Services

ABSTRACT

Location-based services (LBS) require users to continuously report their location to a potentially untrusted server to obtain services based on their location, which can expose them to privacy risks. Unfortunately, existing privacy-preserving techniques for LBS have several limitations, such as requiring a fully - trusted third party, offering limited privacy guarantees and incurring high communication overhead. In this paper, we propose a user-defined privacy grid system called dynamic grid system (DGS); the first holistic system that fulfills four essential requirements for privacy-preserving snapshot and continuous LBS. (1) The system only requires a semi-trusted third party, responsible for carrying out simple matching operations correctly. This semi-trusted third party does not have any information about a user’s location. (2) Secure snapshot and continuous location privacy is guaranteed under our defined adversary models. (3) The communication cost for the user does not depend on the user’s desired privacy level, it only depends on the number of relevant points of interest in the vicinity of the user. (4) Although we only focus on range and k-nearest-neighbor queries in this work, our system can be easily extended to support other spatial queries without changing the algorithms run by the semi-trusted third party and the database server, provided the required search area of a spatial query can be abstracted into spatial regions. Experimental results show that our DGS is more efficient than the state-of-the-art privacy-preserving technique for continuous LBS.

AIM

The aim of this paper is propose a user-defined privacy grid system called dynamic grid system (DGS); the first holistic system that fulfills four essential requirements for privacy-preserving snapshot and continuous LBS

SCOPE

The scope of this paper is show that our DGS is more efficient than the state-of-the-art privacy-preserving technique for continuous LBS.

EXISTING SYSTEM

LBS can be very valuable and as such users should be able to make use of them without having to give up their location privacy. A number of approaches have recently been proposed for preserving the user location privacy in LBS. In general, these approaches can be classified into two main categories.

·      Fully-trusted third party (TTP).

·      Privacy leakage.

·      Service termination

DISADVANTAGES

·      Requiring a fully - trusted third party

·      Offering limited privacy guarantees

·      Incurring high communication overhead

PROPOSED SYSTEM

In this project,  propose a user-defined privacy grid system called dynamic grid system (DGS) to provide privacy-preserving snapshot and continuous LBS. The main idea is to place a semitrusted third party, termed query server (QS), between the user and the service provider (SP). QS only needs to be semi-trusted because it will not collect/store or even have access to any user location information. Semi-trusted in this context means that while QS will try to determine the location of a user, it still correctly carries out the simple matching operations required in the protocol, i.e., it does not modify or drop messages or create new messages. An untrusted QS would arbitrarily modify and drop messages as well as inject fake messages, which is why our system depends on a semi-trusted QS.

ADVANTAGES

·      The system only requires a semi-trusted third party, responsible for carrying out simple matching operations correctly. This semi-trusted third party does not have any information about a user’s location.

·      The communication cost for the user does not depend on the user’s desired privacy level, it only depends on the number of relevant points of interest in the vicinity of the user

 SYSTEM ARCHITECTURE

  

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                 Processor   -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board         -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System      : Android OS                                      

·                Front End                  : JAVA

·                Database                  : Sqlite

·                Tool                           :Eclipse

REFERENCES

Chow, C. ; Huang, Q. ; Wong, D. ; Schlegel, R “USER-DEFINED PRIVACY GRID SYSTEM FOR CONTINUOUS LOCATION-BASED SERVICES” Mobile Computing, IEEE Transactions on  (Volume:PP ,  Issue: 99 ) January 2015

User Privacy and Data Trustworthiness in Mobile Crowd Sensing

ABSTRACT

Smart phones and other trendy mobile wearable devices are rapidly becoming the dominant sensing, computing and communication devices in peoples’ daily lives. Mobile crowd sensing is an emerging technology based on the sensing and networking capabilities of such mobile wearable devices. MCS has shown great potential in improving peoples’ quality of life, including healthcare and transportation, and thus has found a wide range of novel applications. However, user privacy and data trustworthiness are two critical challenges faced by MCS. In this article, we introduce the architecture of MCS and discuss its unique characteristics and advantages over traditional wireless sensor networks, which result in inapplicability of most existing WSN security solutions. Furthermore, we summarize recent advances in these areas and suggest some future research directions.

AIM

The aim of this paper is we introduce the architecture of MCS and discuss its unique characteristics and advantages over traditional wireless sensor networks, which result in inapplicability of most existing WSN security solutions

SCOPE

The scope of this paper is user privacy and data trustworthiness are two critical challenges faced by MCS.

EXISTING SYSTEM

MCS can provide fine grained monitoring of interested parameters without setting up the sensing infrastructure beforehand. Moreover, with the proliferation of mobile wearable devices and the ubiquity of wireless broadband connections, MCS can operate in an environment which is not feasible or economical for WSNs. Second, since mobile wearable devices have much more resources than sensor nodes in terms of computing power, memory, and energy, more requirements can be met by MCS applications. Third, sensing devices in MCS are mobile in nature. Therefore, they can collect spatio-temporal data in a much easier way than traditional WSNs. Fourth, the sensing process is more intelligent as participants can take control of the sensing process. Fifth, sometimes WSNs have high installation and maintenance cost, and possibly insufficient node coverage. However, as MCS leverages existing sensing devices and communication infrastructure, there is virtually no establishment cost.

DISADVANTAGES

1.  User privacy
2.  Data trustworthiness

PROPOSED SYSTEM

In this project, Although a lot of research and development activities on MCS have taken place, they mainly focus on new applications and the solution of data collection. There are a number of other issues that need to be addressed. Among these are user privacy and data trustworthiness. As MCS applications involve data collection across wide geographical areas, spatial-temporal information is invariably associated with the data uploaded by participants. This imposes possible threats to user privacy because the collected data may disclose their locations and trajectories. Other possible privacy invasions include recording intimate discussions and capturing private scenes. Such threats would discourage people from becoming participants in MCS. Since altruistic data collection is a critical element of MCS, this issue of privacy invasion needs to be addressed immediately before the success of MCS is explored further. Another security issue of MCS is the reliability of the uploaded data. As data are reported by participants, they could possibly be falsified. Hence, this raises the issue of data trustworthi-ness. Furthermore, this issue inherently conflicts with the privacy issue. This is because if participants’ identities are not disclosed, those participants reporting falsified or even fabricated data cannot be identified and eliminated. In other words, if full anonymity is provided to MCS participants, guaranteeing the trustworthiness of reported data is difficult. Hence, data trustworthiness in MCS becomes more crucial than in traditional wireless sensor networks (WSNs), which deploy a large number of wireless sensor devices managed by the network owner.

ADVANTAGES

·      Protecting the data trustworthiness counteracts the mechanisms for preserving privacy.

·      A good privacy-preserving reputation system for MCS should consider the link ability exposed by reputation values

SYSTEM ARCHITECTURE

    

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                 Processor   -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board                 -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System      : Android OS                                      

·                Front End                  : JAVA

·                Database                  : Sqlite

·                Tool                           :Eclipse

REFERENCES

Suarez-Tangil, G.,Tapiador, J.E. ; Lombardi, F. ; Di Pietro, R. “ALTERDROID: Differential Fault Analysis of Obfuscated Smartphone Malware”, IEEE Transactions on Mobile Computing Volume PP ,  Issue 99  June 2015