The 21st century witnessed some of the craziest life-changing advancements, including the development of AI and Virtual Reality. All these changes are driven by data, loads and loads of data. At this point of time, data is the most crucial component for the make or break of an organization, and hence, it becomes essential to keep all those data well protected from all sorts of attacks.
In this module, we’ll be discussing some of the challenges faced in big data security and privacy.
Fake data generation
One of the most common issues faced in big data security is fake data generation. The cybercriminals can infiltrate your system and modify it to produce fake results. For example, you have a system that determines the quality of your production process. The attackers can modify it to produce fake results, which ultimately hampers your production process and quality control.
Lack of proper encryption
Encrypting and decrypting big chunks of data is a lengthy and tiresome process. Most of the organization skips this process because of this, and stores the data into the cloud unprotected. This gives easy access to the criminals to extract those data and use it for unethical purposes.
Big data after collection, is subjected to parallel processing like the MapReduce paradigm. In this process, the bulk data is broken down into smaller components, and a mapper process then selects storage for the data. The cybercriminals can modify your data by accessing these mappers' code and can extract sensitive data for illegal purposes.
Data Provenance refers to the collection of all the past data. It is a massive collection of data, and it becomes challenging to secure that information as any slight changes in these data can cause misleading of information, and any undetectable data source can make it more difficult to trace the source of a data breach and fake data manipulation.