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Data Privacy and Security in Data Science: Current State and Future Directions

Data Science is emerging as one of the fastest-growing fields in the technology industry. Many organizations have become data-driven and rely on data-centric decisions. This is made possible due to Data Science. The potential threats in the field of data science are privacy and security. In order to protect the data against breaches, many organizations have ensured practices that will secure their data. There are also certain technologies like Blockchain that should be considered to safeguard the data in the future as well.

 

Scope

  • This article deals with Data Privacy and Security in Data Science.
  • We will comprehend the current state and the future directions that must be incorporated to safeguard the data.

 

Introduction

As technology is advancing, various businesses are opting for data-driven decisions. Almost all companies store and process the data related to the users and the company. Most often, the data is very sensitive to both organizations and users. There are hackers who try to attack sensitive information which could lead to data breaches. Therefore, it is of utmost importance to preserve and maintain data privacy and security. 

 

The data scientists must ensure that the data should remain safe and they should also enforce the techniques that will secure the data. The more the data is collected and analyzed, the more the chance of data breaches will happen. Also, it is important to ensure proper storage of data which cannot be accessed by an unauthorized person. In order to protect the data against breaches, many organizations have ensured practices that will secure their data.

 

Data Privacy and Security: Definition and Importance

In today’s era, the above terms- Data Privacy and Security play a very important role. Data privacy refers to the ability to control the process of how sensitive data is collected, analyzed, and stored in their systems. Data security is another important aspect of data science that deals with protecting the data against vulnerabilities. As data is the most crucial element of data science, it is very important to keep it safe. If the organization fails to do so, they will have to face consequences of the same. Some of the techniques that try to ensure data privacy and security are- encryption, transferring data over secure networks or preventing unauthorized access to others. We will look into the other techniques in detail in the upcoming section. 

 

The current State of Data Privacy and Security in Data Science

The current state of data privacy and security is on the edge right now. On one hand, it is advancing. As a large amount of data is collected and analyzed, it is made easy due to various techniques of data science. This has led to a lot of advancements in various fields. On the other hand, it is important to maintain and protect sensitive information from various attacks and breaches. 

 

There are several rules and regulations that the governments of various countries have levied to ensure to protect the data of their citizens. Some of them are mentioned below-

  • General Data Protection Regulation(GDPR)- Aims to protect the privacy of the people of the European Union.
  • California Consumer Privacy Act(CCPA)- Aims to give the people of California the right to know which data is being collected and where it is used. 

 

As the demand for data science is increasing, the first important step is to store data in a secure place. Data centers or cloud service providers are the probable options. 

In the field of security, the CIA model is a widely accepted model. It stands for Confidentiality, Integrity and Availability. It is used for data privacy and security. 

  • Confidentiality ensures that the data stays confidential and inaccessible to an unauthorized person. Authentication and access control policies are used to ensure data confidentiality. Encryption can be deployed to ensure confidentiality.
  • Integrity is used to ensure that the data is unchanged and should not be lost or modified by unauthorized users. Data integrity can be ensured by techniques like RAID or digital signatures. 
  • Availability ensures that the data should be available to the authorized users as and when needed. In this case, backups can make sure that the data is available all the time. 

 

To incorporate security, encryption is one of the best techniques. It means converting a plain text file to a secret code which can only be deciphered with a decryption key. It can be possible because of various encryption algorithms like Advanced Encryption Standard, RSA, etc. It is also important to ensure that the data should be encrypted in case of transfer from one place to another or within the cloud. Although it is important to know that encryption alone is not sufficient as it might be more prone to brute force attacks, it should be paired with other techniques to improve security.

 

The access control method is one more method that is used to take care of the security of data. From the name, you can guess that access is provided only to those who need to use the data for the necessary work for the company. There are many levels at which it can be implemented. 

 

One more technique that can be used to improve the security of data in the field of data science is generating Adversarial Machine Learning inputs. In this, the data model can be tested against deceptive inputs and see how they respond to it. Here, the data input is manipulated to produce incorrect output results. The above technique can be used to make the model more resistant to adversarial attacks. This method should be used along with some other methods to improve its effectiveness. 

 

Secure multi-party computation(SMPC) is another technique to improve data privacy and security that allows the data to be dealt with among many users. No individual can see the data of the other parties. Multiple parties can work on the data but not reveal it to each other. 

 

One of the most effective techniques is the Blockchain Technology. It is used to incorporate data security and privacy in data science. Blockchain is one technology that is in the boom. Because of this, the information is very sensitive to being hacked or stolen. In blockchain technology, as the name suggests, there is a chain of blocks. Each block is used to store a transaction. After the block is full, the information that is stored in the block is run through an encryption algorithm. This algorithm creates a hexadecimal number called a hash. The block is linked to the previous block thus forming a chain of successive blocks. In this way, the information is secured and cannot easily be tampered with. 

 

As the anonymity of various users can also be a cause for concern, there must be a method that helps to preserve the same. By Data Masking, sensitive information can be hidden and replaced with less sensitive information. It is mostly used for PII, health, or finance-related information. There are various ways in which data masking can be done and it depends on the type of data that has to be secured and protected. 

 

The above were the various techniques to improve data privacy and security in data science. As the field of data science is vast, there are many different ways in which data can be secured but the above ones were the most popular ways to protect data privacy and security in data science. 

 

Future Directions of Data Privacy and Security in Data Science

As mentioned earlier, the world is becoming more data-centric. A majority of companies use Data Science to take informed decisions and improve the efficiency and the progress of the company. In the future, undoubtedly, data science will grow immensely and will be used in almost all fields. As data science is growing, that will require a large amount of data to be processed, and because of the same, there will be more attackers who will try to breach sensitive information and steal the data. It will be of utmost importance to protect the data. 

 

The attackers will come up with new ways to steal the information. Therefore, more advanced and enhanced security features should be implemented. Also, stringent policies should be implemented worldwide to curb the misuse of data. Blockchain is one of the best methods to protect and secure data and should be continued to be the same in the long run. Also, advanced encryption techniques should be deployed to safeguard the data. Cryptography and machine learning can also be used as a solution too. 

 

Conclusion

Summing up, as data science is evolving, it is important to consider the privacy and safety associated with the same. The data scientists must ensure that the data should remain safe and they should also enforce the techniques that will secure the data. The more the data is collected and analyzed, the more the chance of data breaches will happen.

 

To ensure data privacy and security, various techniques can be used, such as encryption, access control, generating Adversarial Machine Learning inputs, Secure Multi-Party Computation (SMPC), and Blockchain technology. However, it is important to keep in mind that no single technique is sufficient to ensure complete security. These techniques should be used in combination to improve their effectiveness.

 

As technology is rapidly advancing, it is important to introduce more advanced and enhanced security features. This will help to protect the information from attackers and make sure the data privacy and security are maintained. 

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