M Tech Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3

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    Privacy Preserving Identity Verification and Verifiable Computation For HealthCare System
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2017) Naik, Shruti Rajendrakumar; Das, Manik Lal
    Paper based medical consulting system has been replaced by electronic health monitoring system due to features like low cost, on demand global accessibility, reliability, accuracy and mobility. Generally, mobile healthcare system collects patient’s sensors data and after processing it gives personal health record(PHR). Computation and communication cost should also be taken into consideration. Hospital provides monitoring program for processing sensors data and providing PHR to patients. Grievously, if millions of patients come at a time then hospital can’t handle their request due to lack of computation power and storage. Therefore, hospital seeks help of cloud for processing of PHR. Although outsourcing comes with a price of security and privacy of data owner. Here, patient has to provide identity to the public domain which should be preserved. Also, cloud can create wrong PHR by mistake, which can dangerously affect patient’s life. Hospital provides it’s monitoring program to cloud, therefore security of monitoring program is also required. In this work 1, we have proposed a protocol for identity verification on cloud side without actually revealing identity of patient and gives verification of PHR.We have analyzed and experimented our protocol that shows, security strength and efficiency comparison with related scheme.
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    Web content outlier detection using latent semantic indexing
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2007) Paluri, Santosh Kumar; Jotwani, Naresh D.
    Outliers are data elements different from the other elements in the category from which they are mined. Finding outliers in web data is considered as web outlier mining. This thesis explores web content outlier mining which finds applications in electronic commerce, finding novelty in text, etc. Web content outliers are text documents having varying contents from the rest of the documents taken from the same domain. Existing approaches for this problem uses lexical match techniques such as n-grams which are prone to problems like synonymy (expressing the same word in different ways), which leads to poor recall (an important measure for evaluating a search strategy). In this thesis we use Latent Semantic Indexing (LSI) to represent the documents and terms as vectors in a reduced dimensional space and thereby separating the outlying documents from the rest of the corpus. Experimental results using embedded outliers in chapter four indicate the proposed idea is successful and also better than the existing approaches to mine web content outliers.