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INTRODUCTION
Wireless sensor networks rely on wireless communication, which is by nature a broadcast medium that is more vulnerable to security attacks than its wired counterpart due to lack of a physical boundary. In the wireless sensor domain, location privacy is an important security issue. Lack of location privacy can expose significant information about the traffic carried on the network and the physical world entities. Various protocols are proposed for routing and data gathering but none of them are designed with security as a goal. The resource limitation of sensor networks poses great challenges for security.
Privacy has been a major security issue in WSN’s, which can be classified into Data-oriented and Context-oriented. Data-oriented protections are categorized into data aggregation and data query techniques. Context-oriented privacy protections can be split into location privacy and temporal privacy techniques, the location privacy is split into data source and base station techniques.
To preserve personal location privacy, we propose two in-network aggregate location anonymization algorithms, namely, resource- and quality-aware algorithms. Both algorithms require the sensor nodes to collaborate with each other to blur their sensing areas into cloaked areas, such that each cloaked area contains at least k persons to constitute a k-anonymous cloaked area. The resource-aware algorithm aims to minimize communication and computational cost, while the quality-aware algorithm aims to minimize the size of the cloaked areas, in order to maximize the accuracy of the aggregate locations reported to the server.
EXISTING SYSTEM
The existing system is based on the concepts like,False Locations, Spatial Cloaking and Space Transformation.Aggregate data privacy that preserves the privacy of the sensor node's aggregate readings during transmission.Data storage privacy hides the data storage location. Query privacy that avoids disclosing the personal interests.Drawbacks of the Existing System are, false location technique, Space transformation technique, and spatial cloaking technique.
1.2PROPOSED SYSTEM
We evaluate our system through simulated experiments. The results show that the communication and computational cost of the resource-aware algorithm is lower than the quality-aware algorithm, while the quality-aware algorithm provides more accurate monitoring services (the average accuracy is about 90%) than the resource-aware algorithm (the average accuracy is about 75%). Both algorithms only reveal k-anonymous aggregate location information to the server, but they are suitable for different system settings. The resource-aware algorithm is suitable for the system, where the sensor nodes have scarce communication and computational resources, while the quality-aware algorithm is favorable for the system, where accuracy is the most important factor in monitoring services.
1.3 Problem Definition:
Individuals whose personal location is being monitored by a third party, are vulnerable to privacy threats. To address this problem, servers can “blacklist” misbehaving users, thereby blocking users without compromising their anonymity. For the location monitoring system using identity sensors, the sensor nodes report the exact location information of the monitored persons to the server, thus using identity sensors immediately poses a major privacy breach.
1) Sensor nodes
Each sensor node determine the number of objects in its sensing area, then blurs its sensing area into a cloaked area ‘m’, which includes at least ‘n’ objects, and reporting ‘m’ with the number of objects located in ‘m’ as aggregate location information to the server.
2) Resource-based blocking
To limit the number of identities a user (Ex: Alice), we have used IP address as resource in our implementation.
3) Server
Server will collect the aggregate locations reported from the sensor nodes, using a spatial histogram to estimate the distribution of the monitored objects, and answering range queries based on the estimated object distribution.
4) System users
Here only authenticated administrators and users can issue range queries to our system through either the server or the sensor nodes. The server uses the spatial histogram to answer their queries.
5) Privacy model
In our system, the sensor nodes constitute a cloaked area and works as defined in our algorithm and communicate with each other through a secure network channel to avoid internal network attacks. Our system also provides anonymous communication between the sensor nodes and the server by employing existing anonymous communication techniques.
6) The pseudonym manager
User must first contact the Pseudonym Manager (PM) and demonstrate control over a resource; for IP-address blocking, the user must connect to the PM directly.
7) The nymble manager
As the PM provided pseudonym to the user and the user connects to the Nymble Manager (NM) through the anonym zing network, and requests nymbles for access to a particular server (such as Google). These nymbles are thus specific to a particular user-server pair.
8) Time
Nymble tickets are bound to specific time periods. While a user’s access within a time period is tied to a single nymble ticket, the use of different nymble tickets across time periods grants the user anonymity between time periods. Smaller time periods provide users with higher rates of anonymous authentication, while longer time periods allow servers to rate-limit the number of misbehaviors from a particular user before he or she is blocked and are blacklisted.
9) Blacklisting a user
If a user misbehaves, the server may link any future connection from this user within the current likability window. A user connects and misbehaves at a server during time period twith likability window w. The server later detects this misbehavior and complains to the NM in time period tc of the same likability window w. As part of the complaint, the server presents the nimble ticket of the misbehaving user and obtains the corresponding seed from the NM. The server is then able to link future connections by the user in time periods of the same likability window w to the complaint. Therefore, once the server has complained about a user, that user is blacklisted for the rest of the day.
3.2 Quality Aware Algorithm
The quality-aware algorithm starts from a cloaked area A, which is computed by resource aware algorithm. Then A will be iteratively updated based on extra communication among the sensor nodes until its area reaches the minimal possible size. For both algorithms, the sensor node reports its cloaked area with the number of monitored persons in the area as an aggregate location to the server.
1) Search space step
Sensor network has a large number of sensor nodes hence it is very costly for a sensor node to gather the information of all the sensor nodes to compute its minimal cloaked area. To reduce the cost, node determines a search space based on the input cloaked area computed by the resource-aware algorithm.
2) The minimal cloaked area step
This step takes a set of peers residing in the search space, S, as an input and computes the minimal cloaked area for the sensor node m. The basic idea of the first optimization technique is that we do not need to examine all the combinations of the peers in S, instead we only need to consider the combinations of at most four peers. Because at most two sensor nodes defines width of MBR and at most two sensor nodes defines height of MBR. It reduces cost by reducing the number of MBR computations among the peers in S.
3) The validation step
This step is to avoid reporting aggregate locations with a containment relationship to the server. We do not allow the sensor nodes to report their aggregate locations with the containment relationship to the server, because combining these aggregate locations may pose privacy leakage.
SIMULATION MODEL
1) Sensor nodes
Each sensor node is responsible for determining the number of objects in its sensing area, blurring its sensing area into a cloaked area A, which includes at least k objects, and reporting A with the number of objects located in A as aggregate location information to the server. Each sensor node is also aware of its location and sensing area.
2) Resource-based blocking
To limit the number of identities a user can obtain, the Nymble system binds to resources that are sufficiently difficult to in great numbers. For ex. We have used IP address as resource in our implementation.
2) Server
The server is responsible for collecting the aggregate locations reported from the sensor nodes, using, using a spatial histogram to estimate the distribution of the monitored objects, and answering range queries based on the estimated object distribution. Furthermore, the administrator can change the anonymized level k of the system at anytime by disseminating a message with a new value of k to all the sensor nodes.
4) System users
Authenticated administrators and users can issue range queries to our system through either the server or the sensor nodes. The server uses the spatial histogram to answer their queries.
CONCLUSION
In this paper, we propose a privacy-preserving location monitoring system and data protection privacy for wireless sensor networks with blocking misbehaving users. We adopt two in-network location anonymization algorithms, namely resource and quality-aware algorithms that preserve personal location privacy and data protection, while enabling the system to provide location monitoring services. The resource aware algorithm mainly aims to minimize the communication and computational cost, whereas quality aware aims to minimize the cloaked area. This paper also provides data privacy from unauthorized users. The data protection provides security to documents, files, etc. The results show that system provides high quality location monitoring and data protection privacy services. In this system servers can blacklist the misbehaving users (unauthorized) from network.