RUN BETTER SAP AG. All rights reserved. 1
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1 RUN BETTER 2013 SAP AG. All rights reserved. 1
2 Project SEEED Processing of Encrypted Data in SAP HANA Internal
3 Outsourcing Data to the Cloud What do you think are the problems? 2013 SAP AG. All rights reserved. Internal 3
4 Outsourcing Data to the Cloud What do you think are the problems? Legally Organisationally Technically Provisioning Lifecycle Management... Security Attacker Models? 2013 SAP AG. All rights reserved. Internal 4
5 Agenda What is Project SEEED? Basic Architecture & Attacker Model DEMO ecolumns / Proxy re-encryption Detailed Architecture in SAP HANA Research Outlook 2013 SAP AG. All rights reserved. Internal 5
6 Processing SQL directly over encrypted data in the cloud is feasible. SEEED is not only encryption at rest but directly processing encrypted data. Different cryptographic algorithms support different SQL operators (select, =, <, >, sum(), count(), groupby(), ). Primary keys NEVER leave the client! Even if the cloud operator makes a memory dump he will not learn anything SAP AG. All rights reserved. Internal 6
7 Searching Encrypted Data in HANA - DEMO aggregating encrypted columns joining encrypted columns User View HANA View 2013 SAP AG. All rights reserved. Internal 7
8 Try it yourself in our Sandbox 2013 SAP AG. All rights reserved. Internal 8
9 Processing Encrypted Data in the Cloud 2013 SAP AG. All rights reserved. Internal 9
10 Process Summary No decryption needed! Keys stay with customer! Encrypted Data Encrypted Result Cleartext Result SQL Query (e.g. Total Sales) Decryption Total Sales: 42 HANA On-Premise 2013 SAP AG. All rights reserved. Internal 10
11 Attacker Model: Technical Consequences Encryption can take place in different layers Traditionally Encrypted Database Client SEEED Client Plaintext Query Optimization and Execution Relational Operators File and Access Methods Buffer Management Disk Space Management Plaintext Encrypted Query Optimization and Execution Relational Operators File and Access Methods Memory Management Persistency Encrypted Disk 2013 SAP AG. All rights reserved. Internal 11
12 Accepting the Trade-offs Performance Security Functionality 2013 SAP AG. All rights reserved. Internal 12
13 Performance Figures (Median of Server Runtimes in Milliseconds for Table size 1,000,000 rows) Test Case SEEED Plain Impact Server-Side Only Exact Search x Equi-Join x Grouping with Aggregation (Sum) x Incl. Client- Side Order by Aggregate (Sum) x TPCH Q4 2, x Q5 1, x 2013 SAP AG. All rights reserved. Internal 13
14 Analysis 2013 SAP AG. All rights reserved. Internal 14
15 Crypto Library Decrypti on UDF SQL Engine Database Tables SEEED Architecture Components Web Application SEEED JDBC Driver Query Rewriting Logic OLAP Engine Encrypted Sum Operator Crypto Library HANA JDBC R Key Store Temporary Database Web Application Server Database Server 2013 SAP AG. All rights reserved. Internal 15
16 SEEED Customers retain full Data Ownership when moving from OP to HANA as a Cloud DB - Obtain guarantees about deletion of cloud data (NB: No feature of SEEED yet, unsure about R&D effort) Secure Deletion of Cloud Data On-Premise to Cloud Provisioning - Automated analysis, labeling data & specifying policies (eg on financial or health care data) - HANA-based massively parallelized Encryption Processing Encrypted Data in the Cloud - Process SQL directly over encrypted data - Enforce access control through cryptography - Allow for standard cloud maintenance operations 2013 SAP AG. All rights reserved. Internal 16
17 SEEED Encryption Cluster Encryption of complete clear text tables Distributed data encryption across several clusters Currently implemented using Apache s Hadoop* Exemplary use case Largest table has 16 columns and 6 Mio rows Total: 9 Tables 14.7 Mio rows Time for encryption Single threaded: ~ 6 months Distributed Encryption: ~ 48 hours *Apache Hadoop: SAP AG. All rights reserved. Internal 17
18 Summary SEEED is not only encryption at rest but directly processing encrypted data. Different cryptographic algorithms support different SQL operators. (select, =, <, >, sum(), count(), groupby(), ). Primary Keys NEVER leave the client! Even if the cloud operator makes a memory dump he will not learn anything. We support the entire SQL syntax and 17 out of 22 TPCH benchmark queries SAP AG. All rights reserved. Internal 18
19 Contact Dr. Andreas Schaad Research Manager Dr. Florian Kerschbaum Chief Expert The Team: Axel, Isabelle, Mathias, Martin H., Walter, Tobias, Patrick, Florian H SAP AG. All rights reserved. Internal 19
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