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You searched for +publisher:"Purdue University" +contributor:("Aniket Kate"). Showing records 1 – 3 of 3 total matches.

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Purdue University

1. Kambatla, Karthik Shashank. Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks.

Degree: PhD, Computer Science, 2016, Purdue University

The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks. Advisors/Committee Members: Ananth Y Grama, Dongyan Xu, Sonia Fahmy, Mathias Payer, Aniket Kate.

Subjects/Keywords: big data; distributed computing; distributed systems

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kambatla, K. S. (2016). Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/1379

Chicago Manual of Style (16th Edition):

Kambatla, Karthik Shashank. “Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks.” 2016. Doctoral Dissertation, Purdue University. Accessed November 18, 2019. https://docs.lib.purdue.edu/open_access_dissertations/1379.

MLA Handbook (7th Edition):

Kambatla, Karthik Shashank. “Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks.” 2016. Web. 18 Nov 2019.

Vancouver:

Kambatla KS. Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2019 Nov 18]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1379.

Council of Science Editors:

Kambatla KS. Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1379


Purdue University

2. Stephen, Julian James. Securing cloud-based data analytics: A practical approach.

Degree: PhD, Computer Science, 2016, Purdue University

The ubiquitous nature of computers is driving a massive increase in the amount of data generated by humans and machines. The shift to cloud technologies is a paradigm change that offers considerable financial and administrative gains in the effort to analyze these data. However, governmental and business institutions wanting to tap into these gains are concerned with security issues. The cloud presents new vulnerabilities and is dominated by new kinds of applications, which calls for new security solutions. In the direction of analyzing massive amounts of data, tools like MapReduce, Apache Storm, Dryad and higher-level scripting languages like Pig Latin and DryadLINQ have significantly improved corresponding tasks for software developers. The equally important aspect of securing computations performed by these tools and ensuring confidentiality of data has seen very little support emerge for programmers. In this dissertation, we present solutions to a. secure computations being run in the cloud by leveraging BFT replication coupled with fault isolation and b. secure data from being leaked by computing directly on encrypted data. For securing computations (a.), we leverage a combination of variable-degree clustering, approximated and offline output comparison, smart deployment, and separation of duty to achieve a parameterized tradeoff between fault tolerance and overhead in practice. We demonstrate the low overhead achieved with our solution when securing data-flow computations expressed in Apache Pig, and Hadoop. Our solution allows assured computation with less than 10 percent latency overhead as shown by our evaluation. For securing data (b.), we present novel data flow analyses and program transformations for Pig Latin and Apache Storm, that automatically enable the execution of corresponding scripts on encrypted data. We avoid fully homomorphic encryption because of its prohibitively high cost; instead, in some cases, we rely on a minimal set of operations performed by the client. We present the algorithms used for this translation, and empirically demonstrate the practical performance of our approach as well as improvements for programmers in terms of the effort required to preserve data confidentiality. Advisors/Committee Members: Patrick T. Eugster, Patrick T. Eugster, Aniket Kate, Mathias Payer, Dongyan Xu.

Subjects/Keywords: Applied sciences; Confidentiality; Data analytics; Integrity; Stream processing; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Stephen, J. J. (2016). Securing cloud-based data analytics: A practical approach. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/949

Chicago Manual of Style (16th Edition):

Stephen, Julian James. “Securing cloud-based data analytics: A practical approach.” 2016. Doctoral Dissertation, Purdue University. Accessed November 18, 2019. https://docs.lib.purdue.edu/open_access_dissertations/949.

MLA Handbook (7th Edition):

Stephen, Julian James. “Securing cloud-based data analytics: A practical approach.” 2016. Web. 18 Nov 2019.

Vancouver:

Stephen JJ. Securing cloud-based data analytics: A practical approach. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2019 Nov 18]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/949.

Council of Science Editors:

Stephen JJ. Securing cloud-based data analytics: A practical approach. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/949


Purdue University

3. Saltaformaggio, Brendan D. Convicted by memory: Automatically recovering spatial-temporal evidence from memory images.

Degree: PhD, Computer Science, 2016, Purdue University

Memory forensics can reveal “up to the minute” evidence of a device’s usage, often without requiring a suspect’s password to unlock the device, and it is oblivious to any persistent storage encryption schemes, e.g., whole disk encryption. Prior to my work, researchers and investigators alike considered data-structure recovery the ultimate goal of memory image forensics. This, however, was far from sufficient, as investigators were still largely unable to understand the content of the recovered evidence, and hence efficiently locating and accurately analyzing such evidence locked in memory images remained an open research challenge. In this dissertation, I propose breaking from traditional data-recovery-oriented forensics, and instead I present a memory forensics framework which leverages program analysis to automatically recover spatial-temporal evidence from memory images by understanding the programs that generated it. This framework consists of four techniques, each of which builds upon the discoveries of the previous, that represent this new paradigm of program-analysis-driven memory forensics. First, I present DSCRETE, a technique which reuses a program’s own interpretation and rendering logic to recover and present in-memory data structure contents. Following that, VCR developed vendor-generic data structure identification for the recovery of in-memory photographic evidence produced by an Android device’s cameras. GUITAR then realized an app-independent technique which automatically reassembles and redraws an app’s GUI from the multitude of GUI data elements found in a smartphone’s memory image. Finally, different from any traditional memory forensics technique, RetroScope introduced the vision of spatial-temporal memory forensics by retargeting an Android app’s execution to recover sequences of previous GUI screens, in their original temporal order, from a memory image. This framework, and the new program analysis techniques which enable it, have introduced encryption-oblivious forensics capabilities far exceeding traditional data-structure recovery. Advisors/Committee Members: Dongyan Xu, Xiangyu Zhang, Dongyan Xu, Mikhail Atallah, Elisa Bertino, Aniket Kate.

Subjects/Keywords: Applied sciences; Android; Cyber forensics; Memory forensics; Program analysis; Security; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Saltaformaggio, B. D. (2016). Convicted by memory: Automatically recovering spatial-temporal evidence from memory images. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/996

Chicago Manual of Style (16th Edition):

Saltaformaggio, Brendan D. “Convicted by memory: Automatically recovering spatial-temporal evidence from memory images.” 2016. Doctoral Dissertation, Purdue University. Accessed November 18, 2019. https://docs.lib.purdue.edu/open_access_dissertations/996.

MLA Handbook (7th Edition):

Saltaformaggio, Brendan D. “Convicted by memory: Automatically recovering spatial-temporal evidence from memory images.” 2016. Web. 18 Nov 2019.

Vancouver:

Saltaformaggio BD. Convicted by memory: Automatically recovering spatial-temporal evidence from memory images. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2019 Nov 18]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/996.

Council of Science Editors:

Saltaformaggio BD. Convicted by memory: Automatically recovering spatial-temporal evidence from memory images. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/996

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