Hiding data so that it’s of no use to attackers is a successful technique to combat big data challenges. In the context of IoT, data masking protects personal information, such as names and addresses that IoT devices collect. Masking this sensitive data makes it more difficult for malicious actors to access and misuse it. Additionally, data masking can also protect sensitive data in transmission between IoT devices and the cloud, helping to secure the overall IoT ecosystem.
Why do we need Data Masking for IoT?
IoT Analytics forecasts that by 2023 there will be an 18% growth in IoT devices, reaching 14.4 billion, and by 2025, this number could potentially rise to 27 billion connected IoT devices.
IoT devices can offer valuable capabilities in commercial settings, but they also pose a significant security risk. Manufacturers often prioritize ease of use and consumer appeal over fundamental security measures, resulting in devices with weak or default credentials.
Wearable and home IoT devices often gather personal information, including biometric data such as voice and gait patterns and personal preferences like eating habits and favorite TV shows. While these devices and the data they collect can offer convenience and benefits to consumers, such as remote control of smart climate systems and personalized workout routines from fitness trackers.
Additionally, an IoT-enabled reception system in a building could automatically verify the identity of visitors and issue them an access card, which would necessitate the collection of personal information.
IIoT ecosystems are primarily focused on industrial applications and typically collect less personal information than regular IoT systems. However, privacy concerns still exist in IIoT. For example, IoT devices in trucks can detect when a driver is fatigued and alert their employer. Wristbands worn by factory workers can also track fidgeting and procrastination, potentially leading to disciplinary actions.
How Can Data Masking Help in IoT?
Since IoT deals with big data, it benefits from masking sensitive data, such as personal information, which helps to protect individuals’ privacy and prevent data breaches. It makes it difficult for malicious actors to access and misuse it, helping to secure the overall IoT ecosystem. With masking, IoT enterprises can create realistic test data sets while ensuring the end-to-end security of sensitive data.
At the same time, it enables IoT management organizations to strictly govern the ecosystem, ensuring data integrity, accuracy, and consistency.
Moreover, many industries are subject to strict regulations, such as HIPAA and GDPR, which require companies to protect sensitive data. Data masking can help companies comply with these regulations. Data masking enables organizations to reduce the costs associated with data breaches, such as legal fees, loss of customers, and damage to reputation.
Using masked data for testing
Humans and applications require data for testing various system functions or standard operating procedures. Using sensitive plaintext data, or original values is risky and increases compliance requirements.
Masked data, when implemented correctly, is an efficient way to test if a system or design will perform as expected in real-world situations.
Using masking to migrate data
Data masking can alter the format of the underlying data. When used in conjunction with an abstraction layer, such as tokenization, masked data can help structure, format or cleanse data to meet new business or schema requirements encountered during migration.
Furthermore, businesses use data masking to minimize human errors that could compromise the security of the data. This way, data masking reduces the potential for such errors. Companies should recognize that not all operations require actual data and can use simulated data to train employees and prepare them for real-life scenarios.
How to Implement Masking in IoT?
Like any other ecosystem dealing with high volumes of data, the IoT deserves a strategic data management approach. Here’s a quick run through the roadmap:
Identifying the sensitive data sets: For example, an individual’s data, financial data etc.
Choose a masking technique: There are several techniques available for masking data, such as tokenization, encryption, and data substitution. Choose the technique that is most appropriate for the type of data you are working with.
Implement masking: Once you have chosen a technique, you’ll need to implement it in your IoT system. This will involve writing code or configuring your system to use the chosen technique.
Test your implementation: Test your implementation to ensure the successful masking of data and that the system is still functioning as expected.
It’s important to continuously monitor your masking technique to ensure that it is still working as expected, and to make sure that the masked data is still usable.
Choosing the right platform
Remember, not just any data management platform can accommodate the big data flowing through the IoT landscape. The appropriate data platform should support both structured and unstructured data, enable dynamic masking and perform in-flight integration. One such data platform that I have tried is called K2view. The platform organizes and integrates fragmented data from various systems using data product schemas, which correspond to specific business entities such as customers, orders, devices, and prescriptions. The platform also masks sensitive information in real-time for each business entity and stores it in an encrypted Micro-Database that can be cached in memory or saved for later use. It efficiently supports a wide range of workloads at a large scale while being significantly more cost-effective and faster than any other option.
Implementing IoT without proper consideration of privacy can lead to negative and unforeseen consequences. As the IoT continues to expand, the amount of data it generates will also increase. This large amount of data can often include personal, health and sensitive information, creating various privacy concerns. Therefore, enterprises must focus on implementing the right data management platforms.
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