Introduction
The Internet of Things (IoT) is an ecosystem consisting of a network of internet-connected devices to gather and exchange data. IoT is now found in many industries and has not remained untapped by any section, from virtual management of warehouses to proactive health monitoring with real-time data. This real-time data is analysed to make informed business decisions and build innovative capabilities in IoT devices that cater to consumers and their demands. Smart devices, from washing machines to smartwatches or other activity trackers, can automatically collect and send valuable user data to improve the performance of the products. 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.
As the volume of generated data and the number of connected devices grows, teams are inevitably challenged regarding data scalability and security. With the increasing number of IoT devices that operate in different environments, it is difficult to comply with a single security standard protocol in a network. Thus, managing sensitive information collected in an IoT ecosystem is essential. The data masking technique hides sensitive information by creating a version of data that looks structurally like the original. The masked data is used for various purposes, such as user training or software testing. The technique also generates data between IoT devices and the cloud without any sensitive data leakage in the overall IoT ecosystem. The data produced by varied devices can be monetized if shared responsibly.
How Data Masking is helpful in an IoT Ecosystem
The IoT ecosystem can have many potential data threats that can compromise the security of the sensitive data generated, stored, and transmitted between IoT devices. Here are some common data threats in the IoT ecosystem:
Malicious firmware or software: IoT devices vulnerable or infected with malicious firmware or software can increase the chances of a data breach. It is thus suggested to modify/update the firmware or software running on the IoT devices to apply the data masking rules to the sensitive data.
Insufficient data encryption: IoT devices continuously transmit information within a network; thus, sensitive data stored or transmitted in unencrypted or unmasked form can lead to theft or misuse.
Data Disposal: The sensitive data stored in IoT devices should be masked or disposed of when no longer needed. Retaining unmasked sensitive data for a period required leads to data leakage and is prone to misuse.
Data masking is helpful to protect sensitive data from the above-mentioned threats in an IoT ecosystem as wearable devices and sensors collect large amounts of data, including personal or confidential information such as location, health data, or financial information. The method of masking or replacing sensitive data with realistic but fictitious ones minimizes the risk of data theft or misuse while allowing authorized users to access the necessary data for analysis and decision-making. Data masking can be applied at different levels in an IoT ecosystem; for example, hiding sensitive data at the device level protects such data/information from potential attacks or misuse while being transmitted to other systems.
How to Implement Data Masking in IoT
Identification of Sensitive Data: In a network of interconnected devices that generate and share data with each other, it is essential to identify sensitive information to ensure that it is masked. Some of the common data types (Sensitive data) for Data Masking are:
PII: Personally Identifiable Information
PHI: Protected Health Information
PCI-DSS: Payment Card Industry Data Security Standard
ITAR: Intellectual Property Information
Determine the right platform: Choosing the right data management platform to accommodate the big data flowing through the IoT landscape is challenging. The platform should be able to manage an increasing number of devices and the type of data flowing in. It should also be compatible with the different systems in the IoT ecosystem and can support interoperability between them.
Different platforms use various data masking techniques as per an enterprise’s requirements. For example, K2view offers data masking through the data product platform; it collects fragmented data from different systems related to specific business entities, such as customers, orders, credit card numbers, etc. This sensitive data is masked for each business entity and is stored in its encrypted Micro-Database. Their solution offers data access control capabilities that can be used to ensure that only authorized users have access to the data they need.
Another popular example is Amazon Web Services (AWS) IoT, which can help to define Transformation rules in the “Rules Engine” for implementing data masking techniques. These rules help obfuscate sensitive data transmitted from IoT devices to the cloud. For example, AWS IoT Transformation rules make masking credit card numbers or other personally identifiable information (PII) easy. This can help ensure that sensitive information is not exposed or compromised during transmission.
Determine the masking technique: Several data masking techniques can be used in an IoT environment to protect sensitive information from unauthorized access, such as randomization, data substitution or redaction, tokenization, encryption, shuffling, etc. The technique may vary depending on the data type and required protection level. For example, Personal Identifiable Information (PII) data can be redacted from a document or an online page – mobile number or credit card number partially or fully to prevent any data leak while maintaining the real structure of the data.
Implement Data Masking Techniques: Once the data masking technique is chosen, it is implemented in IoT devices and systems by applying the masking rules to sensitive data. It is imperative to test the implementation rules applied to sensitive data to ensure that the information is masked and remains functional for its intended purpose.
Monitor Data Masking Technique: It is a good practice to continuously monitor the data masking technique results to ensure that sensitive data remains protected and the masking policies are followed. Most data management platforms offer the monitoring of data masking.
Conclusion
With the growing number of connected devices, the number of potential points of vulnerabilities also increases. The role of data masking in IoT is crucial in ensuring the confidentiality, integrity, and availability of sensitive data. IoT devices and systems can prevent unauthorized access, data breaches, and other security threats by implementing data masking techniques. It is a crucial technique for maintaining data privacy and complying with regulatory requirements such as GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act), thereby improving the overall security and trustworthiness of IoT systems.
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