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Data privacy policy requirements are a quickly evolving part of the data management domain. Healthcare (e.g., HIPAA), financial (e.g., GLBA), and general laws such as GDPR or CCPA impose controls on how personal data should be managed. Relational databases do not offer built-in features to support data management features to comply with such laws. As a result, many organizations implement ad-hoc solutions or use third party tools to ensure compliance with privacy policies. However, external compliance framework can conflict with the internal activity in a database (e.g., trigger side-effects or aborted transactions). In our prior work, we introduced a framework that integrates data retention and data purging compliance into the database itself, requiring only the support for triggers and encryption, which are already available in any mainstream database engine. In this demonstration paper, we introduce DBCompliant – a tool that demonstrates how our approach can seamlessly integrate comprehensive policy compliance (defined via SQL queries). Although we use PostgreSQL as our back-end, DBCompliant could be adapted to any other relational database. Finally, our approach imposes low (less than 5%) user query overhead.
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Harmonizing Privacy Regarding Data Retention and Purging
Data privacy requirements are a complex and quickly evolving part of the data management domain. Especially in Healthcare (e.g., United States Health Insurance Portability and Accountability Act and Veterans Affairs requirements), there has been a strong emphasis on data privacy and protection. Data storage is governed by multiple sources of policy requirements, including internal policies and legal requirements imposed by external governing organizations. Within a database, a single value can be subject to multiple requirements on how long it must be preserved and when it must be irrecoverably destroyed. This often results in a complex set of overlapping and potentially conflicting policies. Existing storage systems are lacking sufficient support functionality for these critical and evolving rules, making compliance an underdeveloped aspect of data management. As a result, many organizations must implement manual ad-hoc solutions to ensure compliance. As long as organizations depend on manual approaches, there is an increased risk of non-compliance and threat to customer data privacy. In this paper, we detail and implement an automated comprehensive data management compliance framework facilitating retention and purging compliance within a database management system. This framework can be integrated into existing databases without requiring changes to existing business processes. Our proposed implementation uses SQL to set policies and automate compliance. We validate this framework on a Postgres database, and measure the factors that contribute to our reasonable performance overhead (13% in a simulated real-world workload).
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- Award ID(s):
- 2016548
- PAR ID:
- 10380895
- Date Published:
- Journal Name:
- Harmonizing Privacy Regarding Data Retention and Purging
- Page Range / eLocation ID:
- 1 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3538712.3538718
