Mummoorthy Murugesan's guest appearances

Mummoorthy Murugesan

CERIAS Weekly Security Seminar - Purdue University

Ryan Henry

Cory Doctorow

Doug Smith

Larry Ponemon

George Heron

Laura Thomas

Eli Lilly

Stephen Elliott

Tony Sager

David Carroll

Jack Daniel

Joe Judge

Chris Reed

Richard III

John Richardson

OwlTail

The rise of enterprise cloud computing has brought an even greater emphasis on data. According to an analysis compiled by Statista, two zettabytes of data were created, captured, copied, and consumed globally in 2010. That figure will reach 97 zettabytes this year and 181 zettabytes by 2025. As the adoption of cloud computing continued to evolve, so did how enterprises approached securing their data. Today, enterprises find their data scattered throughout their various cloud systems, and they have lost visibility into where their sensitive data resides. The problems are about whether there are any shadow data stores that developers left abandoned? Who can access all of the enterprise data on these clouds, and are there excessive privileges? What data is at risk of being breached and falling out of regulatory compliance? Moreover, the growing complexity of cloud computing is a big part of why breached data records have risen (according to the Identity Theft Resource Center) from 16 million in 2010 to more than 155 million today. A recent survey from IDC found that 98% of organizations they queried reported at least one cloud data breach in the past 18 months. With all these challenges, cybersecurity professionals are faced with the daunting task of understanding where their organization's critical or regulated data exists across cloud platforms. The process of identifying and securing cloud data is called the Data Security Posture Management. In this talk, we will go over certain techniques for discovering, analyzing and securing data in various cloud platforms. We will then look at challenging problems that are opening up more avenues for further investigation, and research. About the speaker: Dr. Mummoorthy Murugesan is currently the founding Director of Engineering at Normalyze Inc. Earlier, he worked at Teradata R&amp;D where he developed the incremental planning and execution of queries. He has worked in start-ups such as Netskope, and Turn to build highly scalable systems. At Netskope, he built the data management platform for the CASB (cloud access security broker) product. Before Normalyze, he led the cloud infrastructure initiatives for Workday's Prism analytics. Dr. Murugesan's interests span data, analytics, security and cloud infrastructure. He received his Ph.D. in Computer Science from Purdue University, and Masters degree from Syracuse University.

<p>The rise of enterprise cloud computing has brought an even greater emphasis on data. According to an analysis compiled by Statista, two zettabytes of data were created, captured, copied, and consumed globally in 2010. That figure will reach 97 zettabytes this year and 181 zettabytes by 2025. As the adoption of cloud computing continued to evolve, so did how enterprises approached securing their data. Today, enterprises find their data scattered throughout their various cloud systems, and they have lost visibility into where their sensitive data resides. The problems are about whether there are any shadow data stores that developers left abandoned? Who can access all of the enterprise data on these clouds, and are there excessive privileges? What data is at risk of being breached and falling out of regulatory compliance? Moreover, the growing complexity of cloud computing is a big part of why breached data records have risen (according to the Identity Theft Resource Center) from 16 million in 2010 to more than 155 million today. A recent survey from IDC found that 98% of organizations they queried reported at least one cloud data breach in the past 18 months. With all these challenges, cybersecurity professionals are faced with the daunting task of understanding where their organization's critical or regulated data exists across cloud platforms. The process of identifying and securing cloud data is called the Data Security Posture Management. In this talk, we will go over certain techniques for discovering, analyzing and securing data in various cloud platforms. We will then look at challenging problems that are opening up more avenues for further investigation, and research. About the speaker: Dr. Mummoorthy Murugesan is currently the founding Director of Engineering at Normalyze Inc. Earlier, he worked at Teradata R&amp;D where he developed the incremental planning and execution of queries. He has worked in start-ups such as Netskope, and Turn to build highly scalable systems. At Netskope, he built the data management platform for the CASB (cloud access security broker) product. Before Normalyze, he led the cloud infrastructure initiatives for Workday's Prism analytics. Dr. Murugesan's interests span data, analytics, security and cloud infrastructure. He received his Ph.D. in Computer Science from Purdue University, and Masters degree from Syracuse University.</p>

Mummoorthy Murugesan, Problems and Challenges in Data Security Posture Management

Query-based web search is becoming an integral part of many people's daily activities.  Most do not realize that their search history can be used to identify them (and their interests). In July 2006, AOL released an anonymized search query log of some 600K randomly selected users. While valuable as a research tool, the anonymization was insufficient:  individuals could be identified from the contentsof the queries alone Government requests for such logs serves to increase the concern. To address this problem, we propose a client-centered approach of "plausibly deniable search". Each user query is substituted with a standard, closely-related query intended to fetch the desired results. In addition, a set of k-1 cover queries are issued; these have characteristics similar to the standard query but on unrelated topics. The system provides a property that any of these k queries will produce the same of set of k queries, giving k possible topics the user could have been searching for. We use Latent Semantic Indexing (LSI) based technique to generate queries, and evaluate on the DMOZ webpage collection to show the effectiveness of the proposed approach.

<p>Query-based web search is becoming an integral part of many people's daily activities.  Most do not realize that their search history can be used to identify them (and their interests). In July 2006, AOL released an anonymized search query log of some 600K randomly selected users. While valuable as a research tool, the anonymization was insufficient:  individuals could be identified from the contentsof the queries alone Government requests for such logs serves to increase the concern. To address this problem, we propose a client-centered approach of "plausibly deniable search". Each user query is substituted with a standard, closely-related query intended to fetch the desired results. In addition, a set of k-1 cover queries are issued; these have characteristics similar to the standard query but on unrelated topics. The system provides a property that any of these k queries will produce the same of set of k queries, giving k possible topics the user could have been searching for. We use Latent Semantic Indexing (LSI) based technique to generate queries, and evaluate on the DMOZ webpage collection to show the effectiveness of the proposed approach.</p>

Mummoorthy Murugesan, Providing Privacy through Plausibly Deniable Search