Data sharing is the process of making data available to others for analysis, research or collaboration. Data sharing can have many benefits, such as increasing the transparency, reproducibility and impact of research, fostering innovation and discovery and enabling new insights and solutions. However, data sharing also poses some challenges, especially when the data contains sensitive or confidential information that needs to be protected from unauthorized access, use or disclosure.
This document provides an insight on how our customers can use our data masking solution to enable data sharing without losing the sensitive data around it.
Data Protection Principles
Before sharing data, data owners and data users should be aware of the data protection principles that apply to their data. Data protection principles are the rules and standards that govern how personal or sensitive data should be collected, processed, stored, and shared. Different countries and regions may have different data protection laws and regulations, such as the General Data Protection Regulation (GDPR) in the European Union, the Health Insurance Portability and Accountability Act (HIPAA) in the United States or the Personal Information Protection and Electronic Documents Act (PIPEDA) in Canada. Data owners and data users should comply with the relevant data protection laws and regulations in their jurisdictions and respect the rights and preferences of the data subjects (the individuals whose data is being shared).
Some of the common data protection principles are:
- Data minimization: Data should be collected and shared only to the extent that is necessary and relevant for the purpose of the data sharing.
- Data quality: Data should be accurate, complete, and up-to-date, and any errors or inaccuracies should be corrected or deleted.
- Data security: Data should be protected from unauthorized or unlawful access, use, disclosure, alteration or destruction, using appropriate technical and organizational measures.
- Data confidentiality: Data should be treated as confidential and only accessed or used by authorized persons who have a legitimate need and a clear purpose.
- Data transparency: Data should be shared in a transparent and accountable manner, and data subjects should be informed about the purpose, scope and conditions of the data sharing and their rights and choices regarding the data sharing.
- Data consent: Data should be shared only with the consent of the data subjects unless there is a legal or ethical justification for sharing the data without consent.
Data Sharing Methods
There are different methods for sharing data without losing confidentiality, depending on the type, sensitivity, and format of the data, and the level of access and analysis that the data users need. Some of the common data sharing methods are:
- Data anonymization: Data anonymization is the process of removing or modifying any identifying or sensitive information from the data, such as names, addresses, phone numbers, email addresses, social security numbers, or medical records, so that the data subjects cannot be identified or linked to the data. Data anonymization can be done by using techniques such as masking, hashing, encryption, aggregation, generalization, or perturbation. Data anonymization can reduce the risk of data breaches or misuse, but it may also reduce the quality or utility of the data, and it may not guarantee complete anonymity, as some data may still be re-identified or linked by using other sources of information.
- Data pseudonymization: Data pseudonymization is the process of replacing any identifying or sensitive information from the data with artificial identifiers or codes, such as random numbers, letters, or symbols, so that the data subjects cannot be identified or linked to the data without a key or a map that links the identifiers to the original information. Data pseudonymization can be done by using techniques such as encryption, hashing, or tokenization. Data pseudonymization can enhance the security and confidentiality of the data, but it may not provide full anonymity, as the data may still be re-identified or linked by using the key or the map, or by using other sources of information.
- Data aggregation: Data aggregation is the process of combining or summarizing the data into groups, categories, or statistics, such as averages, totals, counts, or percentages, so that the data subjects cannot be identified or linked to the data. Data aggregation can be done by using techniques such as grouping, binning, or clustering. Data aggregation can reduce the risk of data breaches or misuse, but it may also reduce the granularity or detail of the data, and it may not prevent disclosure of sensitive information, as some data may still be inferred or derived from the aggregated data.
- Data access control: Data access control is the process of restricting or limiting the access or use of the data to authorized persons who have a legitimate need and a clear purpose, and who agree to follow certain rules and conditions for the data sharing. Data access control can be done by using techniques such as passwords, encryption, authentication, authorization, or auditing. Data access control can protect the data from unauthorized or unlawful access, use, disclosure, alteration, or destruction, but it may not prevent data breaches or misuse, as some data may still be leaked, copied, or shared by the authorized persons.
- Data sharing agreement: Data sharing agreement is a formal document that specifies the purpose, scope, and conditions of the data sharing between the data owners and the data users, and the rights and responsibilities of both parties. Data sharing agreement can include information such as the type, format, and size of the data, the method and mode of data transfer, the level and duration of data access, the data security and confidentiality measures, the data quality and integrity standards, the data consent and transparency requirements, the data ownership and attribution rules, the data use and reuse limitations, the data retention and disposal policies, and the data breach and dispute resolution procedures. Data sharing agreement can facilitate the data sharing process and ensure the compliance and accountability of both parties, but it may not guarantee the data protection or quality, as some data may still be misused, lost, or corrupted.
How DataNub Field Masking solution helps to achieve Pseudonymization
One of the data sharing methods that can help to achieve pseudonymization is field masking. Field masking is a technique that replaces or hides some or all of the characters in a field of data, such as a name, an email address, or a phone number, with symbols, characters, or codes, to prevent the identification of the data subject. Field masking can be done by using different methods, such as masking, hashing, tokenization, or encryption. Field masking can preserve the format and the length of the original data and allow the data users to perform some operations or analysis on the masked data, such as sorting, filtering, or aggregating, without revealing the actual data. Field masking can also be reversible or irreversible, depending on the need and the method of masking.
DataNub Field Masking solution is a tool that helps data owners to apply field masking to their data, and data users to access and use the masked data, according to the data sharing agreement. DataNub Field Masking solution allows data owners to select the fields and the methods of masking and generate a masked data field that can be shared with the data users. DataNub Field Masking solution also allows data users to request and receive the masked field access if required, and view and use the masked data in a secure and compliant manner. DataNub Field Masking solution can ensure the pseudonymization of the data, and protect the data confidentiality and privacy, while maintaining the data quality and utility. DataNub Field Masking solution can also support the data audit and traceability and provide the data owners and the data users with the data governance and control.
Please find the below video to see how our solution works.
Conclusion
Data sharing is a valuable and beneficial practice, but it also involves some risks and challenges, especially when the data contains sensitive or confidential information. Data owners and data users should follow the data protection principles and use the appropriate data sharing methods to share data without losing confidentiality. Data owners and data users should also communicate and cooperate with each other and establish a data sharing agreement that defines the terms and conditions of the data sharing. By doing so, data owners and data users can ensure the data protection, quality, and utility, and achieve the goals and benefits of data sharing. Our field masking solution is built to protect the data which are sensitive and our customers can leverage its functionality to achieve the objective.