Big Data could not be described just in terms of its size. However, to generate a basic understanding, Big Data are datasets which can’t be processed in conventional database ways to their size. This kind of data accumulation helps improve customer care service in many ways. However, such huge amounts of data can also bring forth many privacy issues, making Big Data Security a prime concern for any organization. Working in the field of data security and privacy, many organizations are acknowledging these threats and taking measures to prevent them.

WHY BIG DATA SECURITY ISSUES ARE SURFACING

Big data is nothing new to large organizations, however, it’s also becoming popular among smaller and medium sized firms due to cost reduction and provided ease to manage data.

Cloud-based storage has facilitated data mining and collection. However, this big data and cloud storage integration has caused a challenge to privacy and security threats.

The reason for such breaches may also be that security applications that are designed to store certain amounts of data cannot the big volumes of data that the aforementioned datasets have. Also, these security technologies are inefficient to manage dynamic data and can control static data only. Therefore, just a regular security check can not detect security patches for continuous streaming data. For this purpose, you need full-time privacy while data streaming and big data analysis.

PROTECTING TRANSACTION LOGS AND DATA

Data stored in a storage medium, such as transaction logs and other sensitive information, may have varying levels, but that’s not enough. For instance, the transfer of data between these levels gives the IT manager insight over the data which is being moved. Data size being continuously increased, the scalability and availability makes auto-tiering necessary for big data storage management. Yet, new challenges are being posed to big data storage as the auto-tiering method doesn’t keep track of data storage location.

VALIDATION AND FILTRATION OF END-POINT INPUTS

End-point devices are the main factors for maintaining big data. Storage, processing and other necessary tasks are performed with the help of input data, which is provided by end-points. Therefore, an organization should make sure to use an authentic and legitimate end-point devices.

SECURING DISTRIBUTED FRAMEWORK CALCULATIONS AND OTHER PROCESSES

Computational security and other digital assets in a distributed framework like MapReduce function of Hadoop, mostly lack security protections. The two main preventions for it are securing the mappers and protecting the data in the presence of an unauthorized mapper.

SECURING AND PROTECTING DATA IN REAL TIME

Due to large amounts of data generation, most  organizations are unable to maintain regular checks. However, it is most beneficial to perform security checks and observation in real time or almost in  real time.

PROTECTING ACCESS CONTROL METHOD COMMUNICATION AND ENCRYPTION  

A secured data storage device is an intelligent step in order to protect the data. Yet, because most often data storage devices are vulnerable, it is necessary to encrypt the access control methods as well.

DATA PROVENANCE

To classify data, it is necessary to be aware of its origin In order to determine the data origin accurately, authentication, validation and access control could be gained.

GRANULAR AUDITING

Analyzing different kinds of logs could be advantageous and this information could be helpful in recognizing any kind of cyber attack or malicious activity. Therefore, regular auditing can be beneficial.

GRANULAR ACCESS CONTROL

Granular access control of big data stores by NoSQL databases or the Hadoop Distributed File System requires a strong authentication process and mandatory access control.

PRIVACY PROTECTION FOR NON-RATIONAL DATA STORES

Data stores such as NoSQL have many security vulnerabilities, which cause privacy threats. A prominent security flaw is that it is unable to encrypt data during the tagging or logging of data or while distributing it into different groups, when it is streamed or collected.

CONCLUSION

Organizations must ensure that all big data bases are immune to security threats and vulnerabilities. During data collection, all the necessary security protections such as real-time management should be fulfilled. Keeping in mind the huge size of big data, organizations should remember the fact that managing such data could be difficult and requires extraordinary efforts. However, taking all these steps would help maintain consumer privacy.

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Google has released some details from the Security and Privacy team about how Google Play is being curated, and machine learning plays a big part.

Google has two basic goals for applications in the Play Store: safety and exposure. The Security and Privacy team wants to weed out apps with malware, but they’re also concerned about applications that ask for broad permissions that might not be needed. In turn, when good apps that follow good practices are found, the team wants them to be featured in the Play Store.

One of the ways they do this is by using what is called “peer groups”. Applications with similar capabilities are grouped together. Apps like Spotify and Pandora (for example) are different from each other, but they have the same basic functions and are designed to stream music to your Android using details from your account with each service. The same goes for Twitter and Facebook or apps like coloring books. When they do the same basic things, they get lumped together. This makes it easier to study what the apps are doing, how they are doing it, and if they should be doing it at all.

They are then analyzed to see what they request from your device when it comes to personal data. Ideally, every app in a peer group will request the same types of information and have a good reason to do so. But sometimes, one will be an outlier. Google gives the example of a coloring book app that requests fine location details through GPS. Other coloring book apps don’t do this, so one that does would be subject to further review by the Security and Privacy team.

There are too many apps in Google Play for humans to do this effectively, so Google has employed some machine learning techniques to automate much of the process. Deep learning algorithms study the language in the app, data about what the app does and how it does is analyzed by computer, and the peer groups themselves are built by these machines based on things like app metadata and text descriptions as well as metrics like user installs.

Google does plenty to keep malware from getting on your phone through Google Play, but this is also to educate developers about the complex (very) permission model Android uses. this is a pretty cool way to use computers that help users and developers, and it’s great that Google is willing to share some information about how it’s being done.

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