Secured computation: Analyzing sensitive data using homomorphic encryption
Who is this presentation for?
- Data scientists and security engineers
Organizations often work with sensitive information such as social security and credit card numbers. Although this data is stored in encrypted form, most analytical operations ranging from data analysis to advanced machine learning algorithms require data decryption for computation. This creates unwanted exposures to theft or unauthorized read by undesirables. Matt Carothers, Jignesh Patel, and Harry Tang explore a use case that prevents fraudulent victimization of customers, implemented in real time, using secured computations enabled through homomorphic encryption. Fraud victimization is one form of economic crime largely executed by deception; the most common form is identification impersonation or using unauthorized credit cards.
Homomorphic encryption gives organizations a secure and easy way to run analytics on data without having to decrypt it by providing computation capability on the encrypted ciphertext, generating an encrypted result which, when decrypted, matches the result of the operations as if they had been performed on the plaintext. Matt, Jignesh, and Harry focus on two use cases. The first case identifies exact identification match and generating alerts if there is mismatch based on predefined attributes. The second use case covers typographical inputs or communication misinterpretation scenarios by identifying proximal identification matches using a combination of Levenshtein distance and Jaro–Winkler distance. And you’ll leave with a background in high-level solution architecture for secure computation, using containers and GPUs to provide real-time information, and the challenges in working with homomorphic encrypted data and performance.
- Familiarity with data encryption
What you'll learn
- Understand high-level solution architecture for secure computation, using containers and GPUs to provide real-time information, and the challenges in working with homomorphic encrypted data and performance
Matt is a security principal at Cox Communications. He holds patents on weighted data packet communication system, systems and methods of DNS grey listing, and systems and methods of mapped network address translation.
Jignesh Patel is a principal architect at Cox Communications. He has more than 15 years’ experience in applying scientific methods and mathematical models to solve problems concerning the management of systems, people, machines, materials, and finance in industry. Previously, he was a trusted advisor for large software company in the northwest, assisting in data center capacity forecasting and providing machine learning capabilities to detect email spam, predicting DDOS attacks, and preventing DNS blackholes.
Harry Tang is a senior solutions architect in network management service and enterprise data support system organization at Cox Communications. He has more than 20 years of experience in the telecom industry and more than 10 years of experience in large systems architecture and solutions design. His software development expertise spreads into numerous area such as J2EE, SOA, messaging, app server containers, databases (relational database management system (RDBMS) and Columnar NoSQL), Hadoop big data lake, Kubernetes and Docker containers, Telcos OSS, etc. His recent work involves building enterprise data ecosystems in AWS Cloud. Previously, he was at AT&T, focusing on telecom OSS systems solution design and architecture. He has numerous patents officially granted by the US Patent and Trademark Office. He holds a PhD in physics and an MS in computer science.
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