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- All Subjects: Data protection
- Creators: Banerjee, Ayan
Description
With the advent of new advanced analysis tools and access to related published data, it is getting more difficult for data owners to suppress private information from published data while still providing useful information. This dual problem of providing useful, accurate information and protecting it at the same time has been challenging, especially in healthcare. The data owners lack an automated resource that provides layers of protection on a published dataset with validated statistical values for usability. Differential privacy (DP) has gained a lot of attention in the past few years as a solution to the above-mentioned dual problem. DP is defined as a statistical anonymity model that can protect the data from adversarial observation while still providing intended usage. This dissertation introduces a novel DP protection mechanism called Inexact Data Cloning (IDC), which simultaneously protects and preserves information in published data while conveying source data intent. IDC preserves the privacy of the records by converting the raw data records into clonesets. The clonesets then pass through a classifier that removes potential compromising clonesets, filtering only good inexact cloneset. The mechanism of IDC is dependent on a set of privacy protection metrics called differential privacy protection metrics (DPPM), which represents the overall protection level. IDC uses two novel performance values, differential privacy protection score (DPPS) and clone classifier selection percentage (CCSP), to estimate the privacy level of protected data. In support of using IDC as a viable data security product, a software tool chain prototype, differential privacy protection architecture (DPPA), was developed to utilize the IDC. DPPA used the engineering security mechanism of IDC. DPPA is a hub which facilitates a market for data DP security mechanisms. DPPA works by incorporating standalone IDC mechanisms and provides automation, IDC protected published datasets and statistically verified IDC dataset diagnostic report. DPPA is currently doing functional, and operational benchmark processes that quantifies the DP protection of a given published dataset. The DPPA tool was recently used to test a couple of health datasets. The test results further validate the IDC mechanism as being feasible.
Contributorsthomas, zelpha (Author) / Bliss, Daniel W (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Banerjee, Ayan (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2023
Description
Data protection has long been a point of contention and a vastly researched field. With the advent of technology and advances in Internet technologies, securing data has become much more challenging these days. Cloud services have become very popular. Given the ease of access and availability of the systems, it is not easy to not use cloud to store data. This however, pose a significant risk to data security as more of your data is available to a third party. Given the easy transmission and almost infinite storage of data, securing one's sensitive information has become a major challenge.
Cloud service providers may not be trusted completely with your data. It is not very uncommon to snoop over the data for finding interesting patterns to generate ad revenue or divulge your information to a third party, e.g. government and law enforcing agencies. For enterprises who use cloud service, it pose a risk for their intellectual property and business secrets. With more and more employees using cloud for their day to day work, business now face a risk of losing or leaking out information.
In this thesis, I have focused on ways to protect data and information over cloud- a third party not authorized to use your data, all this while still utilizing cloud services for transfer and availability of data. This research proposes an alternative to an on-premise secure infrastructure giving exibility to user for protecting the data and control over it. The project uses cryptography to protect data and create a secure architecture for secret key migration in order to decrypt the data securely for the intended recipient. It utilizes Intel's technology which gives it an added advantage over other existing solutions.
Cloud service providers may not be trusted completely with your data. It is not very uncommon to snoop over the data for finding interesting patterns to generate ad revenue or divulge your information to a third party, e.g. government and law enforcing agencies. For enterprises who use cloud service, it pose a risk for their intellectual property and business secrets. With more and more employees using cloud for their day to day work, business now face a risk of losing or leaking out information.
In this thesis, I have focused on ways to protect data and information over cloud- a third party not authorized to use your data, all this while still utilizing cloud services for transfer and availability of data. This research proposes an alternative to an on-premise secure infrastructure giving exibility to user for protecting the data and control over it. The project uses cryptography to protect data and create a secure architecture for secret key migration in order to decrypt the data securely for the intended recipient. It utilizes Intel's technology which gives it an added advantage over other existing solutions.
ContributorsSrivastava, Abhijeet (Author) / Ahn, Gail-Joon (Thesis advisor) / Zhao, Ziming (Committee member) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2016