Security and Privacy in Big Data Access Controls
This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notable security and privacy aspects required by big data applications. There is a new “paradigm shift” in securing a heterogeneous volume of data generated through high velocity from traditional security mechanisms such as firewalls and delimiting to access control (AC) systems, which are viewed as the fundamental aspects of networking security mechanisms. Enforcing AC becomes even more challenging when researchers and data analysts have to analyze complex and distributed big data (BD) processing cluster frameworks, which are adopted to manage the yottabyte of unstructured sensitive data with restrictions and are most likely to fail due to the malformed AC policy configurations. Modern digital or network forensics helps the security and forensics, which deals with the collection of evidence collected from the Internet. This type of forensic analysis also deals with the cloud and other dispersed environments. As data integrity and data security go side-by-side, they’re separate concepts. Integrity is considered to be whole when staying unchanged or keeping data consistent throughout its life cycle and is a matter of protecting it (security) so that it’s reliable. Data that’s reliable is simply able to meet certain standards, with which compliance is necessary. Cybersecurity experts and analysts are constantly trying to keep pace with changes and trends in the volatile and ever-shifting landscape of IT security. Despite sophisticated tools and solutions that are being rolled out by cybersecurity vendors, data breaches eventually happen – it’s not about the if but the when – and they usually go undetected for a long time. Predictive analytics is gaining momentum in virtually every industry and is enabling organizations to modernize and reinvent the way they do business by looking into the future and obtaining foresight they lacked previously. Real-time predictive analysis precisely predicts what will happen in the future; instead, it forecasts what might happen on the basis of certain “if” scenarios.