Privacy- and Integrity-Preserving Range Queries in Sensor                                    NetworksAbstract:The architec...
mainly stored on storage nodes. Third, query processing becomes more efficientbecause the sink only communicates with stor...
• First, a storage node needs to correctly process encoded queries over      encoded data without knowing their actual val...
Fig 1: Idea of SafeQ for preserving privacy using Magic FunctionsModules:   •   Prefix Membership Verification   •   Submi...
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Privacy and integrity-preserving range queries in sensor networks

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Transcript of "Privacy and integrity-preserving range queries in sensor networks"

  1. 1. Privacy- and Integrity-Preserving Range Queries in Sensor NetworksAbstract:The architecture of two-tiered sensor networks, where storage nodes serve as anintermediate tier between sensors and a sink for storing data and processingqueries, has been widely adopted because of the benefits of power and storagesaving for sensors as well as the efficiency of query processing. However, theimportance of storage nodes also makes them attractive to attackers. In this paper,we propose SafeQ, a protocol that prevents attackers from gaining informationfrom both sensor collected data and sink issued queries. SafeQ also allows a sink todetect compromised storage nodes when they misbehave. To preserve privacy,SafeQ uses a novel technique to encode both data and queries such that a storagenode can correctly process encoded queries over encoded data without knowingtheir values. To preserve integrity, we propose two schemes—one using Merklehash trees and another using a new data structure called neighborhood chains—togenerate integrity verification information so that a sink can use this information toverify whether the result of a query contains exactly the data items that satisfy thequery.Introduction:In this paper, we consider a two-tiered sensor network architecture in whichstorage nodes gather data from nearby sensors and answer queries from the sink ofthe network. The storage nodes serve as an intermediate tier between the sensorsand the sink for storing data and processing queries. Storage nodes bring threemain benefits to sensor networks. First, sensors save power by sending allcollected data to their closest storage node instead of sending them to the sinkthrough long routes. Second, sensors can be memory-limited because data are
  2. 2. mainly stored on storage nodes. Third, query processing becomes more efficientbecause the sink only communicates with storage nodes for queriesDrawbacks:The inclusion of storage nodes also brings significant security challenges. Asstorage nodes store data received from sensors and serve as an important role foranswering queries, they are more vulnerable to be compromised, especially in ahostile environment. A compromised storage node imposes significant threats to asensor network. • First, the attacker may obtain sensitive data that has been, or will be, stored in the storage node. • Second, the compromised storage node may return forged data for a query. • Third, this storage node may not include all data items that satisfy the query.Reasons for the Proposal:We want to design a protocol that prevents attackers from gaining informationfrom both sensor collected data and sink issued queries, which typically can bemodeled as range queries, and allows the sink to detect compromised storage nodeswhen they misbehave. For privacy, compromising a storage node should not allowthe attacker to obtain the sensitive information that has been, and will be, stored inthe node, as well as the queries that the storage node has received, and will receive.Note that we treat the queries from the sink as confidential because such queriesmay leak critical information about query issuers’ interests, which need to beprotected especially in military applications. For integrity, the sink needs to detectwhether a query result from a storage node includes forged data items or does notinclude all the data that satisfy the query. There are two key challenges in solvingthe privacy and integrity-preserving range query problem.
  3. 3. • First, a storage node needs to correctly process encoded queries over encoded data without knowing their actual values. • Second, a sink needs to verify that the result of a query contains all the data items that satisfy the query and does not contain any forged data.EXISTING SYSTEM:The prior art solution to this problem was proposed by Sheng and Li in their recentseminal work. We call it the “S&L scheme.” This scheme has two maindrawbacks:1) It allows attackers to obtain a reasonable estimation on both sensor collecteddata and sink issued queries; and2) The power consumption and storage space for both sensors and storage nodesgrow exponentially with the number of dimensions of collected data.Proposed system:In this paper, we propose SafeQ, a novel privacy- and integrity-preserving rangequery protocol for two-tiered sensor networks. The ideas of SafeQ arefundamentally different from the S&L scheme. To preserve privacy, SafeQ uses anovel technique to encode both data and queries such that a storage node cancorrectly process encoded queries over encoded data without knowing their actualvalues. To preserve integrity, we propose two schemes—one using Merkle hashtrees and another using a new data structure called neighborhood chains—togenerate integrity verification information such that a sink can use this informationto verify whether the result of a query contains exactly the data items that satisfythe query. We also propose an optimization technique using Bloom filters tosignificantly reduce the communication cost between sensors and storage nodes.Furthermore, we propose a solution to adapt SafeQ for event-driven sensornetworks, where a sensor submits data to its nearby storage node only when acertain event happens and the event may occur infrequently.
  4. 4. Fig 1: Idea of SafeQ for preserving privacy using Magic FunctionsModules: • Prefix Membership Verification • Submission Protocol • Query Protocol • Query Processing • Query Execution • Magic function 1 : Hash Map Integrity checker • Magic Function 2: Encryption & DecryptionHardware & Software requirements:Hardware Requirements: • System : Pentium IV 2.4 GHz. • Hard Disk : 40 GB. • Floppy Drive : 1.44 Mb. • Monitor : 15 VGA Colour. • Mouse : Logitech. • Ram : 256 Mb.Software Requirements: • Operating system : - Windows XP Professional. • IDE : - Visual studio 2005 • Coding Language : C# • Back end : Sql Server

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