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Big Data Security with Hadoop
The document discusses the evolution of security features in big data systems, particularly focusing on Hadoop. Key topics include file permissions, authorization, and authentication mechanisms, as well as the importance of preventing unauthorized access and securing data in multi-tenant environments. It also covers security implementations in technologies like Apache Accumulo and Apache HBase, emphasizing encryption for data at rest and the need for granular access controls.
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Big Data Security with Hadoop
- 1. Big Data Security Joey Echeverria | Principal Solu8ons Architect joey@cloudera.com | @fwiffo 1 ©2013 Cloudera, Inc.
- 2. Big Data Security EARLY DAYS 2
- 3. Hadoop File Permissions • Added in HADOOP-‐1298 • Hadoop 0.16 • Early 2008 • Authoriza8on without authen8ca8on • POSIX-‐like RWX bits 3
- 4. MapReduce ACLs • Added in HADOOP-‐3698 • Hadoop 0.19 • Late 2008 • ACLs per job queue • Set a list of allowed users or groups per opera8on • Job submission • Job administra8on • No authen8ca8on 4
- 5. Securing a Cluster Through a Gateway • Hadoop cluster runs on a private network • Gateway server dual-‐homed (Hadoop network and public network) • Users SSH onto gateway • Op8onally can create an SSH proxy for jobs to be submi`ed from the client machine • Provides minimum level of protec8on 5
- 6. Big Data Security WHY SECURITY MATTERS 6
- 7. Prevent Accidental Access • Don’t let users shoot themselves in the foot • Main driver for early features • Not security per-‐se, but a cri8cal first step • Doesn’t require strong authen8ca8on 7
- 8. Stop Malicious Users • Early features were necessary, but not sufficient • Security has to get real • Hadoop runs arbitrary code • Implicit trust doesn’t prevent the insider threat 8
- 9. Co-‐mingle All Your Data • Ofen overlooked • Big data means gegng rid of stovepipes • Scalability and flexibility are only 50% of the problem • Trust your data in a mul8-‐tenant environment • Most cri8cal driver 9
- 10. Big Data Security AN EVOLVING STORY 10
- 11. Authoriza8on • Files • MapReduce/YARN job queues • Service-‐level authoriza8on • Whitelists and blacklists of hosts and users 11
- 12. Authen8ca8on 2.2 High Level Use Cases 2 USE CASES • HADOOP-‐4487 • Hadoop 0.22 and 0.20.205 2.2 High Level Use Cases 1. Applications accessing files on HDFS clusters Non-MapReduce ap- • Late 2010 including hadoop fs, access files stored on one or more HDFS plications, clusters. The application should only be able to access files and services • Based on Kerberos and internal delega8on tokens they are authorized to access. See figure 1. Variations: (a) Access HDFS directly using HDFS protocol. • Provides strong user authen8ca8on servers via the HFTP (b) Access HDFS indirectly though HDFS proxy FileSystem or HTTP get. • Also used for service-‐to-‐service authen8ca8on (joe) Name Node delg(jo e) kerb MapReduce Application kerb(hdfs) Task bloc n k to oke ken ck t Data blo Node Figure 1: HDFS High-level Dataflow 12 2. Applications accessing third-party (non-Hadoop) services Non- MapReduce applications and MapReduce tasks accessing files or opera-
- 13. Encryp8on • Over the wire encryp8on for some socket connec8ons • RPC encryp8on added soon afer Kerberos • Shuffle encryp8on (HTTPS) added in Hadoop 2.0.2-‐ alpha, back ported to CDH4 MR1 • HDFS block streamer encryp8on added in Hadoop 2.0.2-‐alpha • Volume-‐level encryp8on for data at rest 13
- 14. Big Data Security SECURITY FOR KEY VALUE STORES 14
- 15. Apache Accumulo • Robust, scalable, high performance data storage and retrieval system • Built by NSA, now an Apache project • Based on Google’s BigTable • Built on top of HDFS, ZooKeeper and Thrif • Iterators for server-‐side extensions • Cell labels for flexible security models 15
- 16. Data Model • Mul8-‐dimensional, persistent, sorted map • Key/Value store with a twist • A single primary key (Row ID) • Secondary key (Column) internal to a row • Family • Qualifier • Per-‐cell 8mestamp 16
- 17. Cell-‐Level Security • Labels stored per cell • Labels consist of Boolean expressions (AND, OR, nes8ng) • Labels associated with each user • Cell labels checked against user’s labels with a built-‐ in iterator 17
- 18. Pluggable Authen8ca8on • Currently supports username/password authen8ca8on backed by ZooKeeper • ACCUMULO-‐259 • Targeted for Accumulo 1.5.0 • Authen8ca8on info replaced with generic tokens • Supports mul8ple implementa8ons (e.g. Kerberos) 18
- 19. Applica8on Level • Accumulo ofen paired with applica8on level authen8ca8on/authoriza8on • Accumulo users created per applica8on • Each applica8on granted access level of most permi`ed user • Applica8on authen8cates users, grabs user authoriza8ons, passes user labels with requests 19
- 20. Apache HBase • Also based on Google’s BigTable • Started as a Hadoop contrib project • Supports column-‐level ACLs • Kerberos for authen8ca8on • Discussion and early prototypes of cell-‐level security ongoing 20
- 21. Big Data Security FUTURE 21
- 22. Encryp8on for Data at Rest • Need mul8ple levels of granularity • Encryp8on keys 8ed to authoriza8on labels (like Accumulo labels or HBase ACLs) • APIs for file-‐level, block-‐level, or record-‐level encryp8on 22
- 23. Hive Security • Column-‐level ACLs • Kerberos authen8ca8on • AccessServer 23
- 24. 24 ©2013 Cloudera, Inc.