Data Privacy and Security

Organizations are gradually becoming concerned regarding data security in several instances, such as collecting and retaining sensitive information and processing personal information in external environments, which include information sharing and cloud computing. Some of the commonly used solutions, however, do not provide strong and viable protection from privacy disclosures and data theft. Particularly, privacy and risk protection experts are concerned about the security and privacy of data that is used in the process of analytics and then shared externally. As such, organizations need to comply with existing and upcoming regulations for secure processing of sensitive data. From this perspective, new approaches concerning privacy-preserving computing that are clear and sustainable to business processes can present various opportunities while also assisting in finding an appropriate balance between security, privacy, and compliance. 

Encrypting data, especially when at rest, is insufficient when it comes to avoiding data breaches. Ideally, data-at-rest encryption leads to a crypto boundary, an aspect that suggests that data is accessible in plaintext. When it comes to addressing plaintext, it is important to note that it is primarily needed for processing, and this boundary usually exists below the point at which a compromise can be experienced. Additionally, data-at-rest encryption does not necessarily support cases where data sharing with other organizations is done. For data to be deemed useful, it has to be accessible through plaintext within various applications since this plays a significant role in reducing encryption’s protection capability. However, a disadvantage of typical data masking techniques is that they do not largely support the protection of behavioral or transactional data. Notably, the limitations of data masking and data-at-rest have led to a gradual increase in devising new strategies for data protection, especially when advanced approaches tend to protect data with regard to where the traditional encryption and data masking techniques fail. 

Different sectors are embracing advantageous techniques to secure data sharing. This suggests that new privacy-preserving approaches in computing are required when it comes to meeting legal requirements while also providing discretion for data sharing. Taking an example from the healthcare domain, recently, healthcare professionals have found the ability to map the human genome completely, and this has paved the way for endless possibilities for advancements in healthcare. The data from the analysis can primarily test the genetic abnormalities, discover the family history, empower disease-risk analysis as well as the presence of alleles associated with Alzheimer’s. Notably, these studies usually require a vast DNA sample size since they assist in detecting accurate patterns. On the other hand, it is important to note that sharing personal DNA is quite challenging. Most individuals are usually hesitant when sharing personal information with third parties since there is the fear of how and to whom the information will be shared. Additionally, there are legal limitations that are designed to safeguard their privacy, hence restricting the providers from sharing the acquired data. Based on this perspective, homographic encryption techniques are utilized, and they, in turn, enable the citizens to share their genome data while simultaneously retaining their core privacy concerns without necessarily thinking about the traditional all-or-nothing mentality when dealing with third-party providers. 

Data Security and Privacy in Financial Organizations

In a traditional financial institution, the representatives may view at least 25% of their customer's transaction activities. This is based on the idea that a secure platform enhances secure collaborations across business lines since it plays a significant role in speeding up the process, lowering transactional ad operation costs, and detecting more fraudulent activities. As such, gaining these insights requires that individuals should navigate through client information that is considered private. This suggests that private data will be shared between various financial institutions. There are various ways that financial institutions gain an advantage from data sharing, and they include receiving data from third parties, owning outbound data with other institutions, and collaborating data that can be similar to inbound or outbound data. 

When addressing the aspects of inbound data sharing, it is important to note that it allows financial institutions to enhance their systems when it comes to decision-making since more information is provided. In the long term, this leads to higher-quality output while simultaneously supporting more accurate operations. For example, trading companies can incorporate information from third-party corporations with the aim of informing their buy and sell decisions, and this leads to a more accurate understanding of market operations and volatility indices. On the other hand, outbound data sharing provides institutions with an understanding of their capabilities, and hence, customers are provided with other benefits that may be unavailable internally. For instance, Wealth Simple, a robo-advisor organization, provides an opportunity for its clients to access Mint.com securely with the aim of viewing their investment balances, which corresponds to their day-to-day spending, which in turn builds a comprehensive understanding of their financial status. 

Different Data Protection Techniques

There are various data protection techniques, and they include algorithmic techniques, which use encryption keys and encryption algorithms. Differential privacy and k-anonymity models, homomorphic encryption, which offers computing operations of encrypted data, format-preserving techniques, and analytical applications that may require fast search on encrypted data values. Data privacy and security can be divided into three major categories, and each has its pros and cons (Murillo-Zegarra et al., 2020). These categories include field-level data transformations, software-based secure computation algorithms, and architectures that incorporate hardware-based security mechanisms and cryptographic data transformations. However, it is important to note that security and privacy-preserving techniques are costly since they primarily rely on high-tech computer hardware, which is difficult to configure or program directly. 

Secure Multiparty Computation

In a simple multiparty computation, the computations can be done based on the data contributed by several parties. However, not every individual has access to data that is more than what they contributed. Therefore, security and privacy are guaranteed without the need to involve a third party. 

Homomorphic Encryption

Homomorphic encryption plays a significant role in the sphere of privacy-preserving computation techniques. Homomorphic encryption delves into doing away with the compromise that emanates from sharing data while also enhancing security and privacy. Additionally, through Homomorphic encryption, the role of encryption is extended from data at rest and data in transit and is translated to data in use. Therefore, this form of encryption can provide an opportunity for organizations and corporations to leverage the services provided by third-party providers, an aspect that is achieved by mitigating privacy concerns. This is based on the fact that homomorphic encryption provides a means of computing data while it is still encrypted. 

Differential Privacy

In differential privacy, field-level data masking is designed in a way that the available data can be used for querying aggregate statistics while, on the other hand, limiting how much information is limited to individuals. Data-sharing scenarios are supported through this approach based on the fact that it is possible to process data in environments or sources that cannot be trusted (Owusu-Agyemang et al., 2021). Differential privacy can be incorporated into six types of algorithms that are considered transformational. Ideally, they provide arithmetic definitions of how the algorithms hide the absence or presence of any individual’s data in a specific data set. 

K-Anonymity Model

This model ensures that groups that are smaller than the prescribed k number cannot be accessed or identified. K-anonymity is a formal privacy measurement model whose function to ensure that for individual identifiers, there is a class that corresponds to the class that contains at least the k information. On the other hand, the L-diversity enhances the K-anonymity that is associated with data sets that have poor variability. It is designed in a manner that protects the data against deterministic inference attempts that, in turn, ensure that there is an equivalent class that corresponds with the L-diversity attributes (Kang & Cho, 2020). However, it is important to note that this primary variant is susceptible to various attacks, an aspect that led to the development of T-closeness. T-closeness can be described as the enhancement of the L-diversity of data sets, which can be attributed to the unequal distribution where small values are negligible or categorical.

Security and Privacy-Preserving Search on Data

When data is encrypted and outsourced to the cloud, it makes it impossible to conduct a search, an aspect that hinders following up on particular cases. Therefore, there need to be searchable encryption techniques that provide a balance between an organization’s performance, privacy, and functionality. Other than the healthcare and financial spheres, the social media control sphere is largely affected by the security, privacy, and confidentiality of data (Elvy, 2018).

Security, Privacy, and Social Media Control

Social media has a significant role in both daily life and business. So, it is essential to ensure that privacy is protected. Alarmingly, concerns about social media privacy are on the rise, which highlights the need for solutions to data security and how big data is used by advertising businesses. Research indicates that more personal data is now being purchased (Arogyaswamy, 2020). People are compelled to pay a specific price in order to safeguard themselves from data breaches by companies that exploit the data for advertising purposes. From this perspective, data has transformed into a marketable good, necessitating some sort of control over how it is gathered, held, and used for advertising.

Social Media Use

Big data, advertising, and communication between people and businesses have seen substantial changes in the twenty-first century. Prior to the internet's invention, people and businesses had few options to use, especially since it has a large impact on enhancing social media, big data, and advertising. Currently, a number of social media platforms offer expert alternatives for video editing, sharing, data storage, and advertising (Degeling et al., 2020). This presents a big potential to create and distribute great material to millions of people in a matter of seconds. From this perspective, it can be claimed that social media has taken on a fundamental role in how people live as well as how they document and share the physical parts of their lives. Therefore, different social media platforms have had a big influence on how individuals live, maintain relationships, save information, and learn.

Social Media Control and Sustainability

Since social media has grown in popularity over the past ten years, researchers and designers should take a wide range of privacy issues into account that, in turn, promote and enhance meaningful social connections and interactions. Even though the majority of research focuses on disclosing various details, the interpersonal dynamics that are currently connected to social media presence make it essential for people to pay attention to issues beyond informational privacy concerns and instead view privacy as a core concept in terms of interpersonal boundary regulation (Kang & Cho, 2020). According to this viewpoint, it is crucial to achieving the proper level of privacy on social media data by having the power and authority to negotiate how much, how little, and how frequently people choose to interact with others, as well as the type and amount of information that they choose or rather opt to share with others or permit them to share. As a result, it's critical to put in place tools and strategies that evaluate the level of privacy concerns on social media with regard to big data and advertising, as well as take note of the current challenges that people must overcome to fully engage with others and maintain an active social media presence.

The issues relating to moral concerns about privacy are of great significance. On social media and other online platforms, people should ideally pay close attention to their privacy and data protection because they worry that the information they provide — whether explicitly or implicitly will be gathered, compiled, and possibly misused by various corporate entities, the government, and criminals. The ability to exert some level of control over how one is perceived by others is one of the main reasons for the right to data privacy (Arogyaswamy, 2020). Ideally, having a good lot of control makes sure that people can navigate both their lives and their places in the social sphere. Various types of relationships should be upheld in light of various expectations that result from the aspects that are made public. People also require some personal space so they can engage in different activities without worrying about being judged by others (Shah et al., 2021). For this reason, it is essential to have some level of control over the information that is disclosed to others. As a result, corporate organizations have the opportunity to fulfill their corporate social responsibility in regard to data privacy and how they use the information that has been provided, collected, or compiled.

Establishing Safety Practices on Social Media Use, Big Data and Advertising

People frequently contribute different types of data without realizing it by utilizing the internet, computers, and smartphones, to name a few. From this vantage point, data theft is the main cause for concern. Ideally, the majority of the data and details that users divulge on online platforms can be exploited to gain unauthorized access to financial information (Elvy, 2017). As a result, businesses that have been given the authority to keep and secure data should be careful to implement policies that reduce the risk of data breaches. Companies that collect data typically do so in the form of consumer profiles, behaviors, and interests.

Brand Protection and Brand Values

Consumer behavior can be evaluated via social media. However, while using online platforms, privacy concerns and issues arise from computer-generated algorithms that gather, examine, and keep data. When a person shops online, for instance, their data is retained and may be used to create a customized list of products they may be interested in through various advertising platforms. Therefore, it is crucial for businesses to guarantee that customer data is not misused, including by intimidating people into participating in certain activities (Murillo-Zegarra et al., 2020). Since customer data is crucial to advertising, businesses should treat it with the utmost discretion when collecting it.

Scholarly Aims

Data security and privacy are inherently at odds with one another. Social media is currently used in both public and private settings, leading to a variety of moral dilemmas. Since social media ideally blurs the line between the public and private spheres, research indicates that it is crucial to discuss the difficulties associated with using social media and how to overcome them (Vasa & Thakkar, 2022).

Technology, Power, and Governance

The worlds of data and advertising are significantly impacted by technology businesses. Ideally, most businesses have stopped using conventional techniques for gathering and compiling data. This shows that the majority of these businesses aren't thoroughly investigated to make sure they abide by the established laws, rules, and regulations pertaining to data protection and the right to personal privacy. In the long term, these businesses run the risk of abusing their power and utilizing the data they have obtained for things like advertising without first getting permission (Arogyaswamy, 2020).

Encrypting data, especially when at rest, is insufficient when it comes to avoiding data breaches. Ideally, data-at-rest encryption leads to a crypto boundary, an aspect that suggests that data is accessible in plaintext. When it comes to addressing plaintext, it is important to note that it is primarily needed for processing, and this boundary usually exists below the point at which a compromise can be experienced. Additionally, data-at-rest encryption does not necessarily support cases where data sharing with other organizations is done. For data to be deemed useful, it has to be accessible through plaintext within various applications since this plays a significant role in reducing encryption’s protection capability. However, one drawback of common data masking methods is that they don't really support transactional or behavioral data protection. Notably, the shortcomings of data masking and data-at-rest have gradually increased the development of new data protection strategies, particularly when cutting-edge technologies tend to safeguard data in areas where conventional encryption and data masking techniques fall short. When data is encrypted and sent to the cloud, it is impossible to perform a search, which makes it difficult to follow up on specific cases. As a result, searchable encryption solutions are required to strike a balance between an organization's functionality, privacy, and performance. Security, privacy, and data confidentiality have a significant impact on the social media control arena, in addition to the healthcare and financial sectors.