Data Privacy in an Era of Big Data is a major challenge for businesses today, necessitating them to carefully plan how they collect and store personal information to comply with various regulations.
Many beneficial uses of data could be curtailed if all information was subject to privacy regulations, which often fail to recognize different societal values and risks.
What is Big Data?
Big Data refers to large, complex datasets that cannot be handled by traditional data processing applications, creating opportunities to enhance operational processes ranging from manufacturing bottlenecks and customer support optimization, identifying patterns that enable businesses to maximize productivity and efficiency, or improve operational efficiencies.
However, this data could potentially compromise individual privacy protections and compromise anonymity. High profile breaches and increased scrutiny by regulators has highlighted this risk, so companies must provide more clarity as to what data they’re collecting, how it’s being utilized and why this practice is necessary.
Integrating data protection into analytics workflow is also crucial to prevent potential security risks before they manifest themselves, using automated, centralized big data privacy tools which index, inventory and link subjects and identities for continuous risk analysis – this makes complying with regulations like GDPR and CCPA easier.
Why is it important?
Data has become the driving force of economic value creation, efficiency and growth; but with its power comes potential privacy threats. Consumers are losing trust in companies who collect their personal information and are demanding greater control.
Big data presents a challenge when it comes to security and transparency, so data privacy-enhancing technologies play a critical role. Differential privacy provides analysts with access to databases containing personal data while protecting anonymity of individuals within.
As data becomes more centralized and the internet of things (IoT) grows, it’s essential that privacy be integrated into systems from their inception. Data privacy must be embedded in an organization’s strategy and implemented using privacy-by-design techniques to ensure data security measures exist to safeguard collected, analyzed and shared information – otherwise organizations risk consumer distrust as well as regulatory action against their organization.
What are the risks?
Data breaches are one of the main threats posed by Big Data. When large volumes of information are collected without taking adequate security precautions, sensitive information may become vulnerable to hacking and other attacks resulting in identity theft, fraud or other illicit activities.
Potential misuse is another issue to keep an eye out for. For instance, sharing someone’s private data for marketing purposes could result in unwanted or unsolicited emails being sent out by third-party marketers; similarly medical records could be misused to perpetrate insurance scams or launch phishing attacks against patients.
Punagin and Arya (2015) emphasize that privacy concerns arise because data collection occurs across various spheres, with public institutions often lacking the power to impose tighter protections on data that they share with private organizations such as employers requiring potential employees to give personal data during hiring processes and individuals being required to disclose such data when applying for loans or credit cards.
What are the solutions?
Big data offers many benefits, yet also poses numerous threats to individual privacy. Businesses and intelligence agencies routinely collect personal information in order to generate profits or identify threats; unfortunately traditional notions of privacy – informed consent and the ability to remove our information from databases – do not sufficiently protect us against these risks.
With the widespread proliferation of Internet of Things devices (IoT), our personal information may be aggregated and used without our control, leading to significant vulnerabilities in individual privacy, including identity spoofing, eavesdropping, access issues and denial-of-service attacks.
Solution to these issues requires creating a new model for balancing business needs with those of consumers and individuals, while protecting each person’s rights as an individual. This paper will explore both technical and legal obstacles to reaching this goal as well as privacy-preserving technologies available currently and those being developed, legislative processes, consumer/user privacy protection.