Dynamic Column Masking and Row-Level Filtering in HDP
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As enterprises around the world bring more of their sensitive data into Hadoop data lakes, balancing the need for democratization of access to data without sacrificing strong security principles becomes paramount. In this webinar, Srikanth Venkat, director of product management for security & governance will demonstrate two new data protection capabilities in Apache Ranger – dynamic column masking and row level filtering of data stored in Apache Hive. These features have been introduced as part of HDP 2.5 platform release.
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Dynamic Column Masking and Row-Level Filtering in HDP
- 1. 1 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Dynamic Row Filtering and Column Masking with Apache Ranger Srikanth Venkat Senior Director, Product Management
- 2. 2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Disclaimer This document may contain product features and technology directions that are under development, may be under development in the future or may ultimately not be developed. Project capabilities are based on information that is publicly available within the Apache Software Foundation project websites ("Apache"). Progress of the project capabilities can be tracked from inception to release through Apache, however, technical feasibility, market demand, user feedback and the overarching Apache Software Foundation community development process can all effect timing and final delivery. This document’s description of these features and technology directions does not represent a contractual commitment, promise or obligation from Hortonworks to deliver these features in any generally available product. Product features and technology directions are subject to change, and must not be included in contracts, purchase orders, or sales agreements of any kind. Since this document contains an outline of general product development plans, customers should not rely upon it when making purchasing decisions.
- 3. 3 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Agenda Background Dynamic Column Masking and Row Filtering Spark SQL Security via Hive LLAP/Ranger Demo
- 4. 4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Security Challenges of Today’s Data Platforms Central repository of critical and sensitive data – Grey Data Data maintained over long duration – Forever External ecosystem is in flux – The Zoo Users can access and analyze data in new and different ways – Democratization
- 5. 5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Apache Ranger • Central audit location for all access requests • Support multiple destination sources (HDFS, Solr, etc.) • Real-time visual query interface AuditingAuthorization • Store and manage encryption keys • Support HDFS Transparent Data Encryption • Integration with HSM • Safenet LUNA Ranger KMS • Centralized platform to define, administer and manage security policies consistently across Hadoop components • HDFS, Hive, HBase, YARN, Kafka, Solr, Storm, Knox, NiFi, Atlas • Extensible Architecture • Custom policy conditions, user context enrichers • Easy to add new component types for authorization
- 6. 6 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Ranger Architecture HDFS Ranger Administration Portal HBase Hive Server2 Ranger Audit Server Ranger Plugin HadoopComponentsEnterprise Users Ranger Plugin Ranger Plugin Legacy Tools and Data Governance HDFS Knox NifI Ranger Plugin Ranger Plugin SolrRanger Plugin Ranger Policy Server Integration API KafkaRanger Plugin YARNRanger Plugin Ranger PluginStorm Ranger Plugin Atlas Solr
- 7. 8 © Hortonworks Inc. 2011 – 2016. All Rights Reserved ⬢ Simple Intuitive UI for Policy Editing and Setup ⬢ Fine-grained specificity by resource type, user context, tags, and operation ⬢ Supports Access, Tag Based, Dynamic Data Masking, and Row Filtering Policy Types Apache Ranger - Intuitive and Granular Policy Management
- 8. 9 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Apache Ranger Audits - Data Access ⬢ Comprehensive scalable audit logging ⬢ Audits for: ⬢ Resource Access Events with user context ⬢ Policy Edits/Creation/Deletion ⬢ User session information ⬢ Component plugin policy sync operations
- 9. 10 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Row Filtering in Hive R A N G E R Control Access to Rows in Hive Tables based on Context! Goal: Improve reliability and robustness of HDP by providing Row Level Security to Hive tables and reducing surface area of security system ⬢ Capabilities – Restrict data row access based on – user characteristics (e.g. group membership) AND – runtime context ⬢ Access restriction logic at Hive layer => No changes to apps! – Hive applies the access restrictions every time that data access is attempted – Seamless behind the scenes enforcement of row level segmentation without having to add this logic to the predicate of the query – No need for multiple views to filter rows for different groups and users! ⬢ Core Technologies: Ranger, Hive AT L A S H I V E
- 10. 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Row Filtering in Hive R A N G E R Control Access to Rows in Hive Tables based on Context! ⬢ Use Cases: Cross-industry application for data protection: AT L A S H I V E Healthcare • A hospital can create a security policy that allows doctors to view data rows only for their own patients • Insurance claims administrators can view only specific rows for their specific site. Financial Services • A bank can create a policy to restrict access to rows of financial data based on the employee’s business division, locale, or based on the employee’s role • Employees in the finance department are allowed to see customer invoices, payments, and accrual data • European HR employees can see European employee data). Information Technology A multi-tenant application can create logical separation of each tenant’s data so that each tenant can see only their own data rows.
- 11. 12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Dynamic Data Masking of Hive Columns R A N G E R Protect Sensitive Data in real-time with Dynamic Data Masking/Obfuscation! Goal: Mask or anonymize sensitive columns of data (e.g. PII, PCI, PHI) from Hive query output ⬢ Benefits – Does not physically alter the data, or make a copy of it – Original sensitive data also does not leave the data store, but obfuscated when presenting to the user. – No changes are required at the application or Hive layer – No need to produce additional protected duplicate versions of datasets – Simple & easy to setup masking policies ⬢ Core Technologies: Ranger, Hive AT L A S H I V E
- 12. 13 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Dynamic Masking and Row Level Filtering Country National ID CC No Name DOB MRN Policy ID US 232323233 4539067047629850 John Doe 9/12/1969 8233054331 nj23j424 US 333287465 5391304868205600 Jane Doe 8/13/1979 3736885376 cadsd984 Germany T22000129 4532786256545550 Ernie Schwarz 3/5/1963 876452830A KK-2345909 Ranger Policy Enforcement Country National ID CC No MRN Name US xxxxx3233 4539 xxxx xxxx xxxx null John Doe US xxxxx7465 5391 xxxx xxxx xxxx null Jane Doe Country National ID Name MRN Germany T22000129 Ernie Schwarz 876452830A Users from US customer support group see row filtered data for US persons with CC and National ID (SSN) as masked values and MRN is nullified EU Health Policy Admins view relevant columns of data unmasked but are restricted by row filtering policies to see data for EU persons only
- 13. 14 © Hortonworks Inc. 2011 – 2016. All Rights Reserved SparkSQL Security via Hive LLAP
- 14. 15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Spark SQL Security: Row Filtering and Column Masking Spark SQL + Hive enables users to explore very large data sets using SQL Enterprises want to enable Spark SQL for ad-hoc analysis using BI tools with fine grain security Spark provides strong authentication via Kerberos and wire encryption via SSL but as general purpose compute has no built in authorization sub-system Spark also does not have any way to define a pluggable module that contains policies for fine grain authorization – With structured data with columns and rows with Hive, fine grain security becomes a challenge Co-mingled data in the same table may belong to two different groups, each with their own regulatory requirements. Data may have regional restrictions, time based availability restrictions, departmental restrictions, etc. all user passwords: hadoop
- 15. 16 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Hive 2 with LLAP: Open Interfaces
- 16. 17 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Key Features: Spark Column Security with LLAP Fine-Grained Column Level Access Control for SparkSQL. Fully dynamic policies per user. Doesn’t require views. Use Standard Ranger policies and tools to control access and masking policies. Flow: 1. SparkSQL gets data locations known as “splits” from HiveServer and plans query. 2. HiveServer2 authorizes access using Ranger. Per-user policies like row filtering are applied. 3. Spark gets a modified query plan based on dynamic security policy. 4. Spark reads data from LLAP. Filtering / masking guaranteed by LLAP server. HiveServer2 Authorization Hive Metastore Data Locations View Definitions LLAP Data Read Filter Pushdown Ranger Server Dynamic Policies Spark Client 1 2 4 3
- 17. 18 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Example: Per-User Row Filtering by Region in SparkSQL Spark User 2 (East Region) Spark User 1 (West Region) Original Query: SELECT * from CUSTOMERS WHERE total_spend > 10000 Query Rewrites based on Dynamic Ranger Policies LLAP Data Access User ID Region Total Spend 1 East 5,131 2 East 27,828 3 West 55,493 4 West 7,193 5 East 18,193 Dynamic Rewrite: SELECT * from CUSTOMERS WHERE total_spend > 10000 AND region = “east” Dynamic Rewrite: SELECT * from CUSTOMERS WHERE total_spend > 10000 AND region = “west”
- 18. 19 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Agenda Demo
- 19. 20 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Demo Setup Hortonia – mid-size financial services company expanding from US to international markets Employees in EU and US Multiple business units need access to customer data: Analysts, HR Customer data is co-mingled as well as isolated Needs to have rational security policies to provide the right level of access control to customer data across geographies, business functions, and to comply with external regulations (PII, HIPAA, EU Privacy etc.)
- 20. 21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Demo Data Customer data in hortoniabank DB • 2 Customer Tables: 50K customer records each with 38 fields (PII, PHI, PCI & non- sensitive data) –us_customers: USA person data only –ww_customers: multi-language, multi-country, localized person data across the world • 1 Reference table: eu_countries (reference table for looking up EU country codes to country mappings – with BRExit etc.) all user passwords: hadoop
- 21. 22 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Ranger Policies Setup for Demo Only US employees can see data in us_customers table and only from locations within the US (access_us_customers) Only US employees can see data rows of US persons in ww_customers table (filter_ww_customers_table + access_ww_customers) Only EU employees can see rows with EU person data in ww_customers table (filter_ww_customers_table + access_ww_customers) US HR team members can see all original unmasked data (PCI, PII,….) Analysts can view masked versions of sensitive data from WW customers table but are prohibited from viewing PII data in US tables (All masking policies under Masking Tab of Resource based policies) No combination of zip code, MRN, and bloodgroup data are permitted to be joined in any query (prohibition policy)
- 22. 23 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Personas Setup for Demo User Group Access Privileges joe-analyst us_employees, analyst US Data Only, non-sensitive data only, rest masked or forbidden depending on sensitivity kate-hr us_employees, hr US Data Only, All sensitive data (PCI, PII, PHI) ivana-eu-hr eu_employees, hr EU Data Only, All sensitive data
- 23. 24 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Data Column Data Column Description Masking Type Sample Output Ranger Masking Policy password Password Hash 237672b21819462ff39fcea7d990c3e5 mask_password_hash nationalid National ID Show Last 4 xx-xx-9324 mask_nationalid_last4 ccnumber Credit Card Number Show First 4 4532xxxxxxxxxxxx mask_ccnumber_first4 streetaddress Street Address Redact nnn Xxxxxx Xxxxx mask_streetaddress_redact MRN MRN Nullify null mask_mrn_nullify age Age CUSTOM (Adds a random number below 20 to actual age) mask_age_custom birthday Date of Brith CUSTOM 01-01-1987 (Keep year of birth and make date & month 01-01) mask_dob_custom Data Masking Policies setup for us_customers data for analyst group
- 24. 25 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Backup
Notes de l'éditeur
- The Ranger Admin portal is the central interface for security administration. Users can create and update policies, which are then stored in a policy database. Plugins within each component poll these policies at regular intervals. The portal also consists of an audit server that sends audit data collected from the plugins for storage in HDFS or in a relational database. Ranger plugins: Plugins are lightweight Java programs which embed within processes of each cluster component. For example, the Apache Ranger plugin for Apache Hive is embedded within Hiveserver2.These plugins pull in policies from a central server and store them locally in a file. When a user request comes through the component, these plugins intercept the request and evaluate it against the security policy. Plugins also collect data from the user request and follow a separate thread to send this data back to the audit server. User group sync: Apache Ranger provides a user synchronization utility to pull users and groups from Unix or from LDAP or Active Directory. The user or group information is stored within Ranger portal and used for policy definition
- Row level filtering brings convenience to apps running on Hive. By moving the access restriction logic down into the Hive layer, Hive applies the access restrictions every time that data access is attempted, helping simplify authoring of the query and bringing in seamless behind the scenes enforcement of row level segmentation without having to add this logic to the predicate of the query
- Dynamic data masking via Apache Ranger enables security administrators to ensure that only authorized users can see the data they are permitted to see, while for other users or groups the same data is masked or anonymized to protect sensitive content.
- Interactive query: Low latency interactive query, persistent servers ready to process SQL Intelligent in-memory caching Builds on Hive engine + SQL capabilities Long running processes Ability to read from HDFS/S3, cache and serve it out Open interfaces/composable interfaces to read data Extensible interfaces to have Spark to read data out of LLAP and process Rely on LLAP that delivers trusted security Client side mechanisms can be circumvented so we have focused on server side enforcement of security
- Spark has its own exec engine and SQL dialect – so it needs to be able to deal w/ data in a raw manner Delegate all runtime and execution to Spark itself Spark plugin called LLAP context (aware of LLAP daemon, how to read data from LLAP daemon, & aware of Ranger query transformations) Spark SQL issue query, routed to HiveServer 2 into Ranger, Returns split locations Data read in based on split locations in parallel with assigned plan, Ranger applies query transformation to provide column masking and row filtering Then Spark is free to LLAP is trusted daemon