Doctoral Thesis / Dissertation from the year 2019 in the subject Computer Science - Internet, New Technologies, , language: English, abstract: Continuous advancement in software field and widespread use of software products many innovative applications have emerged, cloud computing is one of them. In cloud computing users need not to install software they just log in the cloud and pay for their required service. As many users are frequently using cloud computing a big question arises here is the security of user's personal data present at cloud. Therefore, we need to safeguard the data in the midst of untrusted processes. On keeping these issues in mind, a security model is designed in this thesis. The whole model is divided into three sections: one is data encryption, second is secure data storage and the third one is maintenance of data integrity. In first section before uploading the file on cloud, file is encrypted by RSA Partial Homomorphic algorithm. Two keys public and private are generated after encryption. Between these two keys public key is known to all but private key is known to only authorize users. In second section, the data owner uploads the encrypted file moreover with this one access permission list containing names of authorized user and their respective permission. In this model two access permissions (Read Only and Read and Write) are defined by the data owner. In third section the cloud provider calculates hash value of uploaded file using MD5 hash algorithm. This hash value is transferred back to the data owner to use it for verification purpose. As Owner performs verification, hash value of the desired data present at cloud is again calculated. Now this new hash value matches with old hash value which is present at owner end. If it matches no modification is performed, if hash value does not match then some modifications has been performed on the uploaded data. After uploading the file on cloud this file is visible to all users. They can easily download the file but cannot decrypt it as all users don t have private key. Private key is sent to authenticate users by e-mail so that they can get original data. Data modification is controlled by the owner as cloud stores data in encrypted form. The whole architecture is compared with combination of Triple DES and SHA. The results generated by proposed model have shown that it takes less encryption and decryption time as compare to 3DES and SHA combination. Therefore, the proposed model provides better security and maintains data integrity of the uploaded data on cloud.
Doctoral Thesis / Dissertation from the year 2020 in the subject Computer Science - Commercial Information Technology, Symbiosis International University, language: English, abstract: Data mining is coined one of the steps while discovering insights from large amounts of data which may be stored in databases, data warehouses, or in other information repositories. Data mining is now playing a significant role in seeking a decision support to draw higher profits by the modern business world. Various researchers studied the benefits of data mining processes and its adoption by business organizations, but very few of them have discussed the success factors of decision support projects. The Research Hypothesis states the involvement of the decision tree while adopting accuracy of classification and while emphasizing the impact factor or importance of the attributes rather than the information gain. The concept of involvement of impact factor rather than just accuracy can be utilized in developing the new algorithm whose performance improves over the existing algorithms. We proposed a new algorithm which improves accuracy and contributing effectively in decision tree learning. We presented an algorithm that resolves the above stated problem of confliction of class. We have introduced the impact factor and classified impact factor to resolve the conflict situation. We have used data mining technique in facilitating the decision support with improved performance over its existing companion. We have also addressed the unique problem which have not been addressed before. Definitely, the fusion of data mining and decision support can contribute to problem-solving by enabling the vast hidden knowledge from data and knowledge received from experts. We have discussed a lot of work done in the field of decision support and hierarchical multi-attribute decision models. Ample amount of algorithms are available which are used to classify the data in datasets. Most algorithms use the concept of information gain for classification purpose. Some Lacking areas also exist. There is a need for an ideal algorithm for large datasets. There is a need for handling the missing values. There is a need for removing attribute bias towards choosing a random class when a conflict occurs. There is a need for decision support model which takes the advantages of hierarchical multi-attribute classification algorithms.
