Summary form only given. Sometimes the physically distributed computing devices in a network may be interested in computing some function of their private inputs without disclosing these inputs to one another. This type of computation falls under the category of Secure Multiparty Computation (SMC). The solution to SMC problems in Mobile Ad hoc Networks (MANET) can be found with the modification of the data inputs or with some anonymization technique. MANETs are the wireless networks of the mobile computing devices with no support of any fixed infrastructure. The mobile nodes use any of the radio technology like Bluetooth, IEEE 802.11 or Hiperlan for directly communicating with each other. The nodes behave as hosts as well as routers. The security challenges in the MANET arise due to its dynamic topology, vulnerable wireless link and nomadic environment. An identification mechanism is needed between the nodes using identification and the credentials. This security architecture simultaneously leads to privacy problems. Some mechanism is needed which prevents a node to learn the identity or the credentials of other nodes. To provide location privacy in MANET is a nontrivial task. Current routing protocols do not focus much on the security and the privacy issues. These aspects are postponed till further development. An authentication protocol is needed between nodes using some cryptographic technique. In service-oriented MANET the denial of the service must be taken care of so that the availability of the service is maintained. The security requirement of the ad hoc network depends on its application. For example, for a simple business meeting the requirement is mitigated and for the military battlefield it is severe. Thus no general security architecture can be developed for MANET. Specific security architecture is needed for specific application. Much security related work is still pending and will add to the standards as the physical deployment of the MANET will grow. In this talk, an emphasis is made on how SMC solutions can be used for privacy preservation during computation.
In today’;s distributed computing environment multiple parties compute some function of their private inputs. In such a scenario privacy preservation of such inputs is a matter of great concern because each party is also worried about the privacy of their inputs. This subject is evolved as Secure Multiparty Computation (SMC). The protocols proposed in this paper allow multiple parties to get maximum of their inputs without disclosing individual inputs to one another or even to any third party. We use a third party for computation that may not be trusted. The protocol uses binary countdown approach. In one of the protocols anonymizers are used to hide the identity of the party. The probabilistic analysis of the protocols is presented.
The evolutionary based computational approach has been matured into an extensive collection of concepts and techniques with The hype in The complexities of The problems which could not be easily dealt by The classical approach. The effectiveness and efficiency of these modern approaches are making them popular in The field of research and development. in this paper; we have focused on The involvement of a multi valued logical evolutionary based computational approach in The development of an effective system. this paper describes The area where fuzzy logic can be implemented effectively in system development life cycle. this paper explicitly mentions The application of fuzzy Inference system (FIS) in one of The fact finding techniques of requirement analysis viz. rating scale question method to find The percentage level of The satisfaction of The user of an existing system or a new system
In this paper we propose a new approach to find the optimum learning rate that increases the recognition rate and reduces the training time of the back propagation neural network as well as single layer feed forward Neural Network. We give a comparative analysis of performance of back propagation neural network and single layer feed forward neural network. In our approach we use variable learning rate and demonstrate its superiority over constant learning rate. We use different inner epochs for different input patterns according to their difficulty of recognition. We also show the effect of optimum numbers of inner epochs; best variable learning rate and numbers of hidden neurons on training time and recognition accuracy. We run our algorithm for face recognition application using Principal Component Analysis and neural network and demonstrate the effect of number of hidden neurons and size of feature vector on training time and recognition accuracy for given numbers of input patterns. We use ORL database for all the experiments.
In this paper, we propose a cryptographic solution for secure multi-party computation (SMC). SMC is also called secure function evaluation, which is extensively studied in classical cryptography. SMC protocol allows n participants p(1),...p(n) to compute an n-input data through trusted third party (TTP) without revealing the information of the parties. All the parties may perform secure communication with TTP for computation. During the computation, TTP may undesirably extract data of all the parties for misuse. It may be risky if the data is sent out. To achieve security in SMC, numbers of solutions are available, but they use cryptography as secure communication between the parties and not for the computation. Basically cryptography is used for the secure communication but in our protocol, we use it for the secure communication as well as for computation. TTP computes entire encrypted data received by all the parties without being aware that the data is encrypted so that if data is disclosed by TTP to others even than no one can reveal the actual data of the party. In this protocol, we have tried to remove the concept of hiding the data using anonymizer. Our earlier protocol uses the anonymizer between parties and TTP to hide the identity of parties. Even after enforcing all these securities, the anonymizer can become malicious, causing leakage of data. To prevent this, we require some additional measures. Thus we propose a new cryptographic protocol that causes a secure communication between the parties and the TTP as well as perform secure computation using encryption. The protocol uses a polynomial to encrypt data by party itself before sending to TTP for computation. It also embeds some dummy data at random places. The purpose of dummy data is to check the honesty of TTP because result of dummy data is known to the party. All the data send to TTP is in the form of numbers as it is assumed that TTP performs only arithmetic computation to produce the result. After computation, TTP sends result to respective parties and each party will removes all dummies to get the actual result as well as check the honesty of TTP by dummies. If any other party wants to reveal the actual data then the probability of breaking the data will be of the order 1/m(n), which is quite high and cannot be done in a polynomial time. The security is the most important factor in SMC that is why this protocol meets our requirements. Only limitation of protocol is encryption time. By choosing proper polynomials and their constants, we can reduce the encryption time.
Abstract- Data mining technique; classification and prediction has improved and used in medical domain to helping medical practitioner in making their decisions. As medical data is highly sensitive to personal information of human being; so it is desired to keep private. There are many approaches for classification which have been adapted for privacy preserving in medical data which are based on data separation technique for privacy preserving. There are two scenarios; one is centralized and other is distributed data where several approaches have been developed. In this paper; we propose architecture for privacy preserving in data mining by combining horizontal data distribution and vertical data distribution for breast cancer data set.
The World Wide Web is nearing omnipresence. The explosively growing number of Web contents including Digitalized manuals; emails pictures; multimedia; and Web services require a distinct and elaborate structural framework that can provide a navigational surrogate for clients as well as for servers. Due to the increasing amount of data Available online; the World Wide Web has becoming one of the most valuable resources for information retrievals and knowledge discoveries. Web mining technologies are the right solutions for knowledge discovery on the Web. The knowledge extracted from the Web can be used to raise the performances for Web information retrievals; question answering; and Web based data warehousing. In this paper; we provide an introduction of Web mining as well as a review of the Web mining categories. Then we focus on one of these categories: the Web structure mining. Within this category; we introduce link mining and review two popular methods applied in Web structure mining: HITS and Page Rank