Accumulo Summit 2015: Verifiable Responses to Accumulo Queries [Security]
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Talk Abstract Accumulo requires its users to trust each Accumulo installation with their data—a malicious server or user could easily compromise critical data or learn secrets they are not authorized to access. One particular threat is a malicious Accumulo server tampering with query results by returning forged, modified, or incomplete results to a user. We have implemented a lightweight client-side cryptographic tool to protect Accumulo users from this kind of threat. Our solution is able to handle a spectrum of different threats. At one end of the spectrum, we use end-to-end signatures to guarantee data integrity: Accumulo clients can sign the data they write to Accumulo and verify that the Accumulo instance did not modify it. At the other end of the spectrum, we store metadata about all the entries written to Accumulo, allowing querying clients to guarantee not just the integrity of the elements contained in the query, but that nothing was omitted from the query itself. As an intermediate solution, we propose an extension to the signature scheme that would speed up the signing and verification of entries with symmetric key cryptography, as well as allowing periodic auditing of the database. This work is sponsored by the Department of Defense under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, conclusions and recommendations are those of the author and are not necessarily endorsed by the United States Government. Speaker Cassandra Sparks Associate Technical Staff, Lincoln Laboratory, MIT Cassandra Sparks is a researcher at MIT Lincoln Laboratory. She graduated from Indiana University in 2014 with an MS in computer science, focusing on programming languages and formal methods. Lately, she has been working on cryptographic enforcement of data integrity in Accumulo.
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Accumulo Summit 2015: Verifiable Responses to Accumulo Queries [Security]
- 1. Verifiable Responses to Accumulo Queries Cassandra Sparks Robert K. Cunningham, Ariel Hamlin, Emily Shen, Mayank Varia, David A. Wilson, Arkady Yerukhimovich April 29, 2015 This work is sponsored by the Department of Defense under Air Force Contract FA8721-05-C-002. Opinions, interpretations, recommendations and conclusions are those of the authors and are not necessarily endorsed by the United States Government.
- 2. Verifiable Queries - 2 CS 04/29/15 Introduction to MIT Lincoln Laboratory Established 1951 Lincoln Laboratory is a Department of Defense FFRDC operated by MIT FFRDC: Federally Funded Research and Development Center
- 3. Verifiable Queries - 3 CS 04/29/15 Technology in Support of National Security Sensors Information Extraction Communications Integrated Sensing and Decision Support (Secure – Countermeasure Resistant) Purpose Core Work Areas Space Control Intelligence, Surveillance, and Reconnaissance Systems and Technology Tactical Systems Air and Missile Defense Technology Homeland ProtectionAir Traffic Control Communication Systems Advanced Technology Cyber Security and Information Sciences Engineering Current Mission Areas MIT Lincoln Laboratory Cyber Security and Information Sciences
- 4. Verifiable Queries - 4 CS 04/29/15 Common Big Data Architecture CommandersOperators Analysts Users MaritimeGround SpaceC2 CyberOSINT <html> Data AirHUMINTWeather Analytics A C DE B Computing Web Files Scheduler Ingest & Enrichment Ingest & EnrichmentIngest This talk: cryptographically securing Accumulo
- 5. Verifiable Queries - 5 CS 04/29/15 Threats to Accumulo • Outsourced "cloud" server – Learn content of data/queries – Misattribute data to inserting clients • Malicious insider (likely a sysadmin) – Learn/change data or queries – Misinform honest users • Malicious clients – Make unauthorized queries – Learn stored data – Learn other clients’ queries • External attacker – Insert malware, hack, etc – We won’t detect these, but our crypto provides resiliency Our focus: security against the server
- 6. Verifiable Queries - 6 CS 04/29/15 Querying Clients Secure Accumulo Overview Hadoop Distributed Filesystem Accumulo Zookeeper Network Inserting Clients End-to-end signatures Attribute-based access control Cell-level encryption Verifiable query results System administrator Data at rest encryption TLS encryption Accumulo provides no safeguards! We improve the security of Accumulo with cryptography
- 7. Verifiable Queries - 7 CS 04/29/15 Outline • Introduction • End-to-End Signatures – Digital Signatures – Design Overview – Implementation Details • Verifiable Query Results • Conclusion
- 8. Verifiable Queries - 8 CS 04/29/15 Accumulo Tablet Tablet Server Tablet Tablet Server Tablet Tablet Server Inserts in Accumulo Inserting Client Querying Client ? Row Column Family Column Qualifier Visibilit y Timestamp Value Patient A Hospital 1 Diagnoses Doctor 12349857 …
- 9. Verifiable Queries - 9 CS 04/29/15 • A signature algorithm has three phases: Message Message Key Generation Digital Signatures Signing A signature scheme is secure if an adversary cannot forge a signature for a new message without having the signing key Wrong Message Verification
- 10. Verifiable Queries - 10 CS 04/29/15 Accumulo Tablet Tablet Server Tablet Tablet Server Tablet Tablet Server Digital Signatures in Accumulo Querying Client Row Column Family Column Qualifier Visibility Field Timestamp Value Patient A Hospital 1 Diagnoses Doctor 12349857 … Inserting Client VerifSign
- 11. Verifiable Queries - 11 CS 04/29/15 Signature Code • Implemented in Python as a client-side wrapper – Uses the pyaccumulo library – No server-side modifications needed • Currently in the process of being open-sourced – Contact pace-contact@ll.mit.edu for updates • Several interesting design choices: – Where to store the signature metadata? – There are many signature algorithms—which one to use?
- 12. Verifiable Queries - 12 CS 04/29/15 Storing Signature Metadata • How do we store the signature of each cell? Option 1: Separate table Option 2: Value field Option 3: Visibility Field Pro: original table is unmodified Con: twice as many reads & writes Pro: value field is good at storing unstructured data Con: interferes with iterators Patient Records Patient 1 Flu shot Patient 2 Broken knee Patient 3 Chicken pox Doctor Admin Admin Patient Records Signatures Patient 1 <signature 1> Patient 2 <signature 2> Patient 3 <signature 3> Doctor Admin Admin Patient Records Patient 1 <signature 1>|Flu shot Patient 2 <signature 2>|Broken knee Patient 3 <signature 3>|Chicken pox Doctor Admin Admin Patient Records Patient 1 Flu shot Patient 2 Broken knee Patient 3 Chicken pox Doctor|“<signature 1>” Admin|“<signature 2>” Admin|“<signature 3>” Pro: all Accumulo functionality still works Con: interferes with visibility label evaluation optimizations We support all three options
- 13. Verifiable Queries - 13 CS 04/29/15 Signature Algorithm Options We support RSA and ECDSA signatures, and are investigating how to safely use MACs Option 1: RSA Signatures Option 2: Elliptic Curve Signatures (ECDSA) Option 3: Message Authentication Codes • Fast signature verification • Large signature & key size • Fast signature creation • Relatively small signature & key sizes • Symmetric key---uses the same key for signing & verification • Much faster than RSA and ECDSA • Con: one malicious client has more power to interfere with integrity
- 14. Verifiable Queries - 14 CS 04/29/15 Performance (curve secp256r1) Benchmarked on a virtualized single-node Accumulo 1.7.0 instance
- 15. Verifiable Queries - 15 CS 04/29/15 Security Summary: Signatures • Signatures allow clients to verify data integrity – Malicious server cannot modify or fabricate results • Signatures cannot verify data completeness – Server could omit both data & signature to avoid detection Modification Insertion Omission Signatures can detect:
- 16. Verifiable Queries - 16 CS 04/29/15 Outline • Introduction • End-to-End Signatures • Verifiable Query Results – Merkle Hash Trees – Authenticated Skip Lists • Conclusion
- 17. Verifiable Queries - 17 CS 04/29/15 The digest is a small value (constant size) that represents the entire dataset digest Authenticated Data Structures • Data structures that allow provably correct queries – Correctness defined relative to a trusted, well-known source – Need to support range queries VO Inserting Client Accumulo Server Querying Client ? VO ADS ADS: Authenticated Data Structure VO: Verification Object
- 18. Verifiable Queries - 18 CS 04/29/15 digest Merkle Hash Trees 2 4 6 8 h(2) h(4) h(6) h(8) a = h(h(2), h(4)) b = h(h(6), h(8)) e = h(a, b) 10 12 14 16 h(10) h(12) h(14) h(16) c = h(h(10), h(12)) d = h(h(14), h(16)) f = h(c, d) root = h(e, f) Digest is the root node’s hash value
- 19. Verifiable Queries - 19 CS 04/29/15 Merkle Hash Trees 2 4 6 8 h(2) h(4) h(6) h(8) a = h(h(2), h(4)) b = h(h(6), h(8)) e = h(a, b) range(5, 9) 10 12 14 16 h(10) h(12) h(14) h(16) c = h(h(10), h(12)) d = h(h(14), h(16)) f = h(c, d) root = h(e, f) Naïve solution allows a malicious server to omit elements at the ends of ranges Part of the range returned Part of the verification object Computed based on returned information
- 20. Verifiable Queries - 20 CS 04/29/15 Naïve Merkle Tree Security Omitting internal query results Signatures: Naïve MHTs: Solution: return boundaries of the range Omitting boundary query results
- 21. Verifiable Queries - 21 CS 04/29/15 Merkle Hash Trees, Revisited 2 4 6 8 h(2) h(4) h(6) h(8) a = h(h(2), h(4)) b = h(h(6), h(8)) e = h(a, b) range(5, 9) 10 12 14 16 h(10) h(12) h(14) h(16) c = h(h(10), h(12)) d = h(h(14), h(16)) f = h(c, d) root = h(e, f) Part of the range returned Part of the verification object Computed based on returned information
- 22. Verifiable Queries - 22 CS 04/29/15 Security Summary: ADSs Signatures: Naïve MHTs: MHTs: Omitting internal query results Omitting boundary query results
- 23. Verifiable Queries - 23 CS 04/29/15 Merkle Hash Tree Disadvantages • Mostly used for static data • How to insert elements into MHTs? Approach 1: Unbalanced Insert Approach 2: Balanced Insert Linear time operations! Linear time insert!
- 24. Verifiable Queries - 24 CS 04/29/15 Authenticated Skip Lists O(log(n)) O(log(n)) (expected) O(n) O(log(n)) (expected) O(log(n)) O(log(n)) (expected) MHT Skip List Lookup Insert Verify Randomized skip lists have empirically better performance than other tree-like data structures
- 25. Verifiable Queries - 25 CS 04/29/15 Outline • Introduction • End-to-End Signatures • Verifiable Query Results • Conclusion
- 26. Verifiable Queries - 26 CS 04/29/15 Additional Work • Confidentiality to hide data from the server & unauthorized users – Per-cell encryption allows flexible encryption for different use cases – Cryptographically enforcing Accumulo’s visibility labels with key management • Using HMACs for better performance without sacrificing security • Key management and distribution for all cryptographic components
- 27. Verifiable Queries - 27 CS 04/29/15 Conclusion • Signatures for data tampering detection – Currently implemented in Python – Client-side library – Contact pace-contact@ll.mit.edu to be notified when the code is open- sourced • Authenticated Data Structures for full query correctness checks – Working on embedding in Accumulo for greater efficiency Questions?