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Overview of Big Data Security

It is expected that data volumes will rise ever higher in the future. A study from IBM predicted that there would be as many as 2.72 million data scientist workers ready to support companies cope with this amount of data, which is proven accurate. The increased usage of big data would affect the understanding and application of business intelligence and its security by organizations. Security is a significant concern these days, independent of fields and technologies.

As compared to other areas Big Data to have securities issues and attacks happening every single minute, these attacks can be on different components of Big Data, like on stored data or the data source.

Big Data is all about large and complex data sets, which can be both structured and unstructured. Click to explore about, What is Big Data: Characteristics, Challenges, Tools & Use Cases

What is Big Data Security?

It's the collective term for all the measures and tools used to guard both the data and analytics methods from attacks, theft, or other malicious activities that could cause a problem or negatively affect them. Like other forms of attacks, Big Data can be compromised either by attacks originated from online or offline spheres.

Why Big Data Security is important?

Today almost every organization is thinking of adopting Big Data as they see the potential and utilizing the power of Big Data; they are using Hadoop to process these large data sets and securing your data is the most important step they are concern about; independent of organization sizes, everyone is trying to secure their data. As a Hadoop process, multiple types of data are combined and stored in a Hadoop data lake, and then the stored information is processed accordingly.

As it saves a different kind of data from various sources, so we need to make security essential as almost every enterprise that are using big data has some form of sensitive data, which needs to be protected. Sensitive data can be the user’s credit card details, banking details, passwords. Big data is not a small thing, and we can’t describe it in the context of size, as size is one of the main features of Big Data. To secure it, someone can construct various strategies like keeping out unauthorized users and intrusions with firewalls, making user authentication reliable, giving training to end-user training, and many others.

With the advent of Big Data, the structured approach fails miserably to cater to the needs of the humongous information processing that tends to be unstructured in nature. SQL vs NoSQL vs NewSQL: The Full Comparison

What are the Challenges in Securing Big Data?

There are several challenges to protecting big data that can jeopardize its safety. It's important to note that these issues aren't limited to on-premise Big data systems. They even have to do with the cloud. Take nothing for granted when hosting the big data platform in the cloud. With good security service level arrangements, the vendor needs to resolve these same obstacles.

Typical Challenges to Securing Big Data

  1. Newer innovations in active growth include advanced computational methods for unstructured Big data and non-relational databases (NoSQL). Protecting these new toolsets for security technologies and procedures can be complicated.
  2. Data ingress and storage are well-protected with advanced encryption software. They may not have the same effect on data production from multiple analytics platforms to multiple locations.
  3. Administrators of Big Data systems can decide to mine data without obtaining permission or warning. Regardless of whether the motive is interest or criminal benefit, the monitoring software must track and warn on suspicious access.
  4. The sheer scale of a Big Data installation, which can range from terabytes to petabytes, makes regular security audits impossible. Since most large data platforms are cluster-based, which exposes several nodes and servers to several vulnerabilities.
  5. The Big Data owner is at risk of data loss and disclosure if the environment's protection is not updated regularly.
  6. Big data protection professionals must be proficient in cleanup and know how to delete malware. Security software must detect and alert suspicious malware infection on the system, database, or web.
various major challenges that come into the way while dealing with Big Data which need to be taken care of with Agility.Click to explore about, Big Data Challenges and Solutions

What are the top 10 Best Practices for Securing Big Data?

The below listed are the top 10 Best Practices for Securing Big Data:

Safeguard Distributed Programming Frameworks

To begin, create trust by using methods like Kerberos Authentication and ensuring that predefined security policies are followed. The data is then "de-identified" by decoupling all personally identifiable information (PII) from it, ensuring that personal privacy is not jeopardized.

Then, using mandatory access control (MAC), such as the Sentry tool in Apache HBase, you allow access to files based on a predefined security policy and ensure that untrusted code does not leak information through device resources. After that, the hard part is done; all left is to maintain the system to prevent data leakage. In a cloud or virtual environment, the IT department should be scanning worker nodes and mappers for bogus nodes and altered duplicates of results.

Secure Non-Relational Data

Non-relational databases, such as NoSQL, are common, but they're vulnerable to NoSQL injection attacks. By encrypting or hashing passwords and maintaining end-to-end encryption by using algorithms such as advanced encryption standard (AES), RSA, or Safe Hash Algorithm 2.

Secure Data Storage and Transaction Logs

Storage control is a critical component of Big Data reliability. By using signed message digests to have a cryptographic identifier for each digital file or record and to use a technique known as a secure untrusted data repository (SUNDR) to detect unauthorized file modifications by malicious server agents

Endpoint Filtering and Validation

Using a mobile device management solution, you can use trusted credentials, perform resource verification, and link only trusted devices to the network. Using statistical similarity detection and outlier detection strategies, you can process malicious inputs while defending against Sybil attacks (one person posing as several identities) and ID-spoofing attacks.

Real-Time Compliance and Security Monitoring

Organizations can use techniques like Kerberos, safe shell, and internet protocol protection to get a grip on real-time data by using Big Data analytics. It's then simple to monitoring logs, set up front-end security mechanisms like routers and server-level firewalls, and start putting security controls in place at the cloud, network, and application levels.

Graph Databases uses graph architecture for semantic inquiry with nodes, edges, and properties to represent and store data. Role of Graph Databases in Big Data Analytics

Preserve Data Privacy

Employee awareness training centers on new privacy laws and ensures that information technology is kept up to date by using authorization processes. In addition, data leakage from different databases can be regulated by analyzing and tracking the infrastructure that connects the databases.

Big Data Cryptography

Mathematical cryptography has improved significantly. Enterprises can run Boolean queries on encrypted data by creating a method to scan and filter encrypted data, such as the searchable symmetric encryption (SSE) protocol.

Granular Access Control

The two main aspects of access management are limiting and allowing user access. The key is to create and execute a policy that automatically selects the best option in any given situation.

To set up granular access controls:

  1. Immutable elements should be denormalized, and mutable elements should be normalized.
  2. Please keep track of confidentiality provisions to make sure they're followed.
  3. Keep track of control marks.
  4. Keep track of administrative information.
  5. To ensure proper data federation, use a single sign-on (SSO) and a labeling system.

Strategies for Granular Access Control, some are listed below :

  1. Point out mutable elements and immutable elements.
  2. Access labels should be maintained, track admin data too.
  3. Use single sign-on, and maintain a proper labeling scheme.
  4. Perform audit layer/orchestrator

Granular Auditing

In Big Data protection, granular auditing is essential, particularly after a system attack. Organizations should develop a unified audit view following an attack and include a complete audit trail with quick access to the data to reduce incident response time.

The integrity and security of audit records are also important. Audit data should be kept isolated from other data and safeguarded with granular user access controls and routine reporting. When configuring auditing, keep Big Data and audit data separate, and allow all necessary logging. An orchestrator tool like ElasticSearch can make it easier to do.

Data Provenance

It's provenance metadata that Big Data applications produce. This is a different kind of data that requires special protection. Creating an infrastructure authentication protocol that manages access and sets up daily status alerts, and constantly checks data integrity with checksums.

Hadoop File System (HDFS) is a distributed file system. All types of files can be stored in the Hadoop file system. Taken from Article, Introduction to Data Serialization in Apache Hadoop

What are the best Big Data Security Tools?

Big Data Security should meet four critical criteria – perimeter security and authentication framework, authorization and access, data privacy, and audit and reporting.

  • Authentication –  Required for guarding access to the system, its data, and services. Authentication makes sure the user is who he claims to be. Two levels of Authentication need to be in place – perimeter and intra-cluster -  Knox Kerberos.
  • Authorization -  Required to manage access and control over data, resources, and services. Authorization can be enforced at varying levels of granularity and in compliance with existing enterprise security standards.
  • Centralized Administration and Audit - It is required to maintain and report activity on the system. Auditing is necessary for managing security compliance and other requirements like security forensics. - Ranger
  • Data at rest/in-motion Encryption - It is required for control of unauthorized access to sensitive data either while at rest or in motion. Data protection should is considered at the field, file, and network level, and appropriate methods should be adopted for the security - HDFS and Wire encryption.

What is the architecture of Big Data Security?

Basic Architecture to secure any big data platform contains different stages as follows:

  1. Data Classification: In this phase, training data set is provided to a classification algorithm to categorize data into two categories, such as normal and sensitive, by considering different types of possible attacks and history of usage data.
  2. Sensitive Data Encryption: At this step, sensitive data is encrypted with a homomorphic cryptosystem such as Pailliers cryptography.
  3. Data Storage Using ORAM Technology: This stage focuses on storing normal and encrypted sensitive data on separate system nodes using the ORAM technique.
  4. Data Access through Path Hiding Approach: During this phase, any end-user seeking specific data can utilize the path hiding technique to obtain the data while ensuring data privacy. The path hiding technique prevents third parties from guessing data access patterns, thereby securing the overall system.
Java vs Kotlin
Our solutions cater to diverse industries with a focus on serving ever-changing marketing needs. Click here for our Big Data Consulting Services and Solutions

Compressive Approach to Big Data Security

In the digital age, when data is increasing rapidly in almost every aspect of human life, data security has become very important. The sector is huge and diverse in social networking sites, healthcare, retail sector, and education. Almost everywhere, digitization is taking place, and so are the challenges of security. A security breach can potentially happen at any level of data processing, so the security concern and its potential solution have been suggested at every level, starting from data accumulation to data storage, data analysis, and data processing. So best practices and measures are needed to be taken to secure the data in a good manner.

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