Now with “NoSQL” and “Big Data” Open Source trends in the market place Security finally has an intersection… a union if I may where new solutions to solve problems that have plagued our society can finally begin to arrise (and have already in many cases). Fraud, Malware, Phishing, Spam, etc all can be tackled now with new Security solutions because of Big Data and Open Source.
At the front lines of this is Apache Accumulo which is a Big Data, Open Source and Secure NoSQL Database that runs on top of Apache Hadoop. It was originally developed by the United States National Security Agency and submitted to the Apache Foundation as Open Source in 2011 with 3 years of development and production operation already having occurred.
Accumulo extends the BigTable data model to implement a security mechanism known as cell-level security. Every key-value pair has its own security label, stored under the column visibility element of the key, which is used to determine whether a given user meets the security requirements to read the value. This enables data of various security levels to be stored within the same row, and users of varying degrees of access to query the same table, while preserving data confidentiality.
SECURITY LABEL EXPRESSIONS
When mutations are applied, users can specify a security label for each value. This is done as the Mutation is created by passing a ColumnVisibility object to the put() method:
Text rowID = new Text("row1"); Text colFam = new Text("myColFam"); Text colQual = new Text("myColQual"); ColumnVisibility colVis = new ColumnVisibility("public"); long timestamp = System.currentTimeMillis(); Value value = new Value("myValue"); Mutation mutation = new Mutation(rowID); mutation.put(colFam, colQual, colVis, timestamp, value);
SECURITY LABEL EXPRESSION SYNTAX
Security labels consist of a set of user-defined tokens that are required to read the value the label is associated with. The set of tokens required can be specified using syntax that supports logical AND and OR combinations of tokens, as well as nesting groups of tokens together.
admin audit system These can be specified alone or combined using logical operators: // Users must have admin privileges: admin // Users must have admin and audit privileges admin&audit // Users with either admin or audit privileges admin|audit // Users must have audit and one or both of admin or system (admin|system)&audit
When both | and & operators are used, parentheses must be used to specify precedence of the operators.
When clients attempt to read data from Accumulo, any security labels present are examined against the set of authorizations passed by the client code when the Scanner or BatchScanner are created. If the authorizations are determined to be insufficient to satisfy the security label, the value is suppressed from the set of results sent back to the client.
Authorizations are specified as a comma-separated list of tokens the user possesses:
// user possess both admin and system level access Authorization auths = new Authorization("admin","system"); Scanner s = connector.createScanner("table", auths);
Each accumulo user has a set of associated security labels. To manipulate these in the shell use the setuaths and getauths commands. These may also be modified using the java security operations API.
When a user creates a scanner a set of Authorizations is passed. If the authorizations passed to the scanner are not a subset of the users authorizations, then an exception will be thrown.
To prevent users from writing data they can not read, add the visibility constraint to a table. Use the -evc option in the createtable shell command to enable this constraint. For existing tables use the following shell command to enable the visibility constraint. Ensure the constraint number does not conflict with any existing constraints.
config -t table -s table.constraint.1=org.apache.accumulo.core.security.VisibilityConstraint
Any user with the alter table permission can add or remove this constraint. This constraint is not applied to bulk imported data, if this a concern then disable the bulk import permission.
SECURE AUTHORIZATIONS HANDLING
For applications serving many users, it is not expected that an accumulo user will be created for each application user. In this case an accumulo user with all authorizations needed by any of the applications users must be created. To service queries, the application should create a scanner with the application users authorizations. These authorizations could be obtained from a trusted 3rd party.
Often production systems will integrate with Public-Key Infrastructure (PKI) and designate client code within the query layer to negotiate with PKI servers in order to authenticate users and retrieve their authorization tokens (credentials). This requires users to specify only the information necessary to authenticate themselves to the system. Once user identity is established, their credentials can be accessed by the client code and passed to Accumulo outside of the reach of the user.
QUERY SERVICES LAYER
Since the primary method of interaction with Accumulo is through the Java API, production environments often call for the implementation of a Query layer. This can be done using web services in containers such as Apache Tomcat, but is not a requirement. The Query Services Layer provides a mechanism for providing a platform on which user facing applications can be built. This allows the application designers to isolate potentially complex query logic, and enables a convenient point at which to perform essential security functions.
Several production environments choose to implement authentication at this layer, where users identifiers are used to retrieve their access credentials which are then cached within the query layer and presented to Accumulo through the Authorizations mechanism.
Typically, the query services layer sits between Accumulo and user workstations.
New software as a service solutions will start to spring up into the market as will new out of the box open source solutions. Whether we are trying to prevent health care fraud, protect individuals from identify theft or corporations from intrusion all without comprimsing the (C)onfidentiality, (I)ntegrity and the (A)vailability of the data and distributes systems.
Gentle Introduction of Hadoop and Big Data!
This Hadoop book was written with following goals and principles: Make Hadoop accessible to a wider audience -- not just the highly technical crowd. There are a few unique chapters that you won't find in other Hadoop books, for example: Hadoop use cases, Hadoop distributions rundown, BI Tools feature matrix.