WHY SHOULD BUSINESSES BE CONCERNED ABOUT DATA PRIVACY IN IOB?
- Data privacy is gaining traction as a result of IoB
The Internet of Behavior (IoB) is an extension of the Internet of Things (IoT). IoB combines technologies that are focused on tracking people’s locations and facial recognition, integrating the data, and mapping it to behavioral activities. IoB does this by analyzing data acquired from users’ online activities from a behavioral psychology standpoint. As a result, customers were more aware of data breaches, intrusive advertising practices, and persistent security concerns. Following this, regulators and authorities began to pay greater attention to and crack down on unethical data harvesting techniques more frequently.
03 reasons why businesses should be concerned about data security and privacy in the IoB era:
– Data protection laws such as the GDPR and the CCPA compel businesses to keep consumer data safe. Data breaches can result in hefty fines for businesses. According to the DLA Piper GDPR Data Breach Survey 2020, GDPR fines totaled more than US$126 million between May 2018 and January 2020.
– Because your company lacks self-sufficiency in analytics and application testing, data may need to be examined by third-party entities. PETs allow for data sharing while maintaining anonymity.
– Businesses or customers (depending on your company strategy) may wish to quit dealing with your brand as a result of privacy breaches. One example is the drop in Facebook’s stock price following the Cambridge Analytica scandal.
While firms will continue to acquire data, transparency in how it is used, stored, and shared will increase consumer trust. Privacy integration with existing marketing technologies can be automated with the correct solutions, and data preferences can be synced across digital assets to create smarter marketing campaigns.
- Enterprise data privacy methods and solutions
It is also insufficient for businesses to just remove identifiable information from their web assets; anonymous use must be completely anonymous. Differential privacy and the use of machine learning to create synthetic data are two methods for achieving this (ML).
Artificial intelligence (AI) can also be used to improve existing privacy-protecting systems, such as vendor management, cookie and consent monitoring, and data management processes. Companies should, however, exercise caution to ensure that AI and machine learning systems are well-developed enough to prevent bias and discrimination.
Users will be able to manage their privacy settings from a central location using privacy dashboards or a centralized preference management tool. How personal data is used, collected, and if consumers can totally remove or amend their data are all features that can be supplied. While some solutions allow for the erasure of extracted data, real-time data deletion and editing is still in the works.
Privacy Enhancing Technologies (PETs) will ensure that users’ personal information is protected while data is analyzed. Homomorphic encryption and Secure multi-party computation (SMPC) are two technologies that make this possible.
– Homomorphic Encryption: this technology allows operations to be conducted on encrypted data that produce the same results as if the operations were done on unencrypted data. This way, a corporation can share data with another for analysis without losing its anonymity or privacy, because the data is simply in an unintelligible format.
– Secure Multi-Party Computation: This cryptographic technology is a subclass of the previous one, allowing complex computational or analytical processes to be done on a larger volume of encrypted data, allowing for the use of “machine learning” models. It’s already in use at firms like Google and Facebook, and it’s in products like the TensorFlow machine learning tool, which allows models to be trained with encrypted third-party data. Companies exchange their encrypted data with a third party for this reason, who analyzes it and gives back the results without jeopardizing the content’s privacy. One of the fields with the most evident application for this is healthcare.
Access to limited data, as in a hospital or where private technology is stored, often needs to be limited. Data Access Control is aimed to replace Role-based Access Control for privacy while regulating how personal information is used when certain data items are accessed. Distributed blockchain technologies can be a wonderful approach to do so.
In order to determine the particular demands of their systems, companies should examine the security and solution vendors before they choose. In order to advise management and collaborate closely with vendors to guarantee that acquired solutions are appropriate, effective and effective for the company’s needs, it is crucial for organizations with IT professionals with technical competence.
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