As the global data production rate is estimated to be 59 zettabytes per day, it is understandable that businesses are increasingly investing in big data solutions. By unlocking the potential of such a substantial amount of data, companies can benefit from invaluable insights that can be applied to a wide range of objectives, regardless of the sector or target market. Consequently, it is not shocking that approximately 63% of companies have already adopted big data solutions.
It is anticipated that big data usage will see an increase in the years ahead. The rate of growth, however, will be dependent upon how effectively organizations manage the various challenges that accompany the utilization of big data. These include financial implications, the requirement for high-level data specialists, technical requirements, and strategic considerations. Despite the fact that these issues are widely recognized, many companies are still not paying sufficient attention to the further, equally pressing concern of data privacy.
Data misuse by large corporations is not a novel occurrence. Despite this, many companies endeavor to meet the minimum required standards, while simultaneously developing new legal regulations to ensure the proper use of data for analytics and business purposes. Neglecting to take this seriously, however, can have major implications for a business, such as a reduction in profits and a loss of reputation.
Businesses should take the privacy implications of their big data initiatives seriously, and develop a comprehensive plan to mitigate any potential risks. To do this effectively, it is important to understand the privacy implications that big data can bring, and to be aware of the strategies that can be employed to better protect sensitive data.
Main Privacy Concerns Concerning Big Data
In order to create an effective strategy for protecting personal data, it is essential to have a comprehensive understanding of the most common risks. Having an understanding of potential threats can greatly increase a company’s chances of success in the face of adversity. The most significant dangers to privacy in the age of big data are:
Hacking into Computer systems:
The information, behaviors, and interests of consumers are a vital source of insights from Big Data. However, due to the sensitive nature of this data, it is paramount that any large data systems have robust security measures in place. Any weak defenses, poorly-designed systems, or malicious attacks can lead to a data breach and should be guarded against.Information exchange:
Many organizations exist solely to compile data, store it in databases, and then offer these databases to other businesses. If the algorithms used for this process are faulty, it could lead to incorrect and unsecured data. Selling inaccurate data, otherwise known as brokerage, can swiftly compromise the buyer’s large data initiatives and the confidentiality of their customer’s information.Customization of information:
When it comes to protecting people’s identities in the age of big data, one of the most significant considerations is understanding how to draw conclusions from data that has been anonymized, making it difficult to trace back a specific set of data to a person. However, collecting anonymous data is often not achievable as there is usually a way to link a particular set of records to a person, potentially violating their right to privacy.
Data privacy is at risk due to human error, either unintentional or intentional, and poor decision making at a strategic level. To address these issues, it is important to take a comprehensive approach and implement established strategies.
Methods Most Appropriate For Protecting The Confidentiality Of Huge A
Data privacy is a sensitive topic, yet businesses should not be discouraged from utilising big data. There are various beneficial outcomes that may arise from successful integration. Although there may be certain risks, there are measures that can be taken to reduce their effect to a minimum, making privacy concerns highly unlikely.
Following are examples of such procedures:
Homomorphic Encryption
Data analysis is a key element of big data. Unfortunately, the process can result in private information becoming available to the public, which could put the security of those connected to the data at risk. To mitigate this, homomorphic encryption can be employed by businesses. This allows for data to be processed by big data algorithms without having to be decrypted first, leading to quicker and more precise insights. Additionally, any findings are also encrypted, making it more difficult for malicious parties to comprehend any intercepted data.
Reducing the Overload of Information
It is illogical to assume that ‘big data’ techniques should only be used with very large datasets. Instead, organizations should focus on ensuring that the data they collect is as useful as possible, and not simply gathering data for the sake of it. To ensure the data being gathered is necessary, it is essential to evaluate the collecting approach.
Using Real-Time Tracking
It is essential to take prompt action to prevent damage from security breaches. Malicious actors may cause significant disruption to a company’s systems and data stores if left unchecked. To mitigate this risk, it is essential to implement systems that monitor critical components of the online environment and raise alerts if any anomalies are detected.
The Constant Training of Employees
It is paramount to recognize that human error is the primary source of the majority of security incidents. Hence, organizations processing extensive amounts of data should implement regular security training for their personnel. This will assist in equipping staff with the knowledge to avoid data breaches, such as through the use of strong passwords, enhanced security procedures, and training to detect potential threats (particularly those related to social engineering).
Protecting Against Dangers From Within
Employees who are acting maliciously towards the organization, regardless of the motivation, constitute an insider threat, which goes beyond simply inexperienced members of staff. It is therefore essential that security measures are put into place to counter this risk, such as separation of roles and least privilege, robust procedures for employee termination, implementation of a BYOD policy, and regular risk assessments.
Effortless Exchange of Information
In summary, customers and users may feel betrayed if they are not aware of the conditions under which their data is used, if the wording is misleading, if it is concealed from them, or if there is no option to opt-out. For this reason, businesses should be clear and transparent when gathering and using customer data, providing an explanation of what data is being collected, why it is necessary, and how it will benefit them. As well as this, companies should also provide customers with the option to opt-out of the sharing of their personal data.
The Tailor-Made Methodology
It is essential to be aware that the strategies listed above are general approaches that any business can take to improve user privacy when using large data sets. However, it should be noted that these are not the only methods available; depending on the nature of their operations, different companies may have distinct requirements.
It is essential to have a well-defined, three-step plan for securing privacy in big data initiatives. First, the importance of robust safeguards for privacy must be recognized. Second, the organization must develop a unique strategy tailored to its specific requirements. Finally, the strategy must be implemented, monitored and adjusted to ensure the highest level of privacy is maintained. This is the only viable approach to address the privacy issue.
