tailieunhanh - Artificial Intelligence for Wireless Sensor Networks Enhancement Part 15

Tham khảo tài liệu 'artificial intelligence for wireless sensor networks enhancement part 15', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Security and Privacy in Wireless Sensor Networks 409 After computing R1 the source node c1 performs neighborhood discovery to find out the other source nodes it is connected to. This information c1 passes to the server. Server keeps the knowledge of the nodes already participated. If the source nodes connected to c1 is not already participated the server randomly chooses one of those non-participated source nodes and sends that message to c1. Let this next source node be c2. Now accordingly c1 passes R1 to c2. The source node c2 computes R2. R2 1 x2 modP The source node follows the same procedure as c1 and sends R2 to c3. This way cN is reached which computes RN. Rn Rm- xN modP The server when it finds out that all the nodes are participated it asks the last node to send Rn to it. Server now directs the first source node c1 to compute the summation as X Rn r1 modP The source node after computing the summation sends that value to the server. The server may process it or sends that value for further processing. Ukil and Sen Ukil Sen 2009 considers a scenario where data aggregation needs to be done in privacy-preserved way for distributed computing platform. There are number of data sources which collect or produce data. The data collected or produced by the sources is private and the owner or the source does not like to reveal the content of the data. But the collected data from the source is to be aggregated by an aggregator which may be a third party or part of the network where the data sources belong. The data sources do not trust the aggregator. So the data needs to be secure and privacy protected. The computation for the aggregation is based on the concept of SMC. SMC allows parties with similar background to compute results upon their private data minimizing the threat of disclosure. Consider a set of parties who neither trust each other nor the channels by which they communicate. Still the parties wish to correctly compute some common function of their local