More Like this

1. Purging Compliance from Database Backups by Encryption

   https://doi.org/10.26421/JDI3.1-4  

   Scope, Nick; Rasin, Alexander; Lenard, Ben; Wagner, James; Heart, Karen (February 2022, Journal of Data Intelligence)

   Data compliance laws establish rules intended to protect privacy. These define both retention durations (how long data must be kept) and purging deadlines (when the data must be destroyed in storage). To comply with the laws and to minimize liability, companies must destroy data that must be purged or is no longer needed. However, database backups generally cannot be edited to purge ``expired'' data and erasing the entire backup is impractical. To maintain compliance, data curators need a mechanism to support targeted destruction of data in backups. In this paper, we present a cryptographic erasure framework that can purge data from across database backups. We demonstrate how different purge policies can be defined through views and enforced without violating database constraints. 

   more » « less
2. The Intersection of Compliance, Databases, and IT Operations

   Lenard, Ben; Rasin, Alexander; Scope, Nick; Al_Johani, Thamer (July 2024, The 36th International Conference on Scientific and Statistical Database Management (SSDBM))

   Most organizations rely on relational database(s) for their day-to-day business functions. Data management policies fall under the umbrella of IT Operations, dictated by a combination of internal organizational policies and government regulations. Many privacy laws (such as Europe’s General Data Protection Regulation and California’s Consumer Privacy Act) establish policy requirements for organizations, requiring the preservation or purging of certain customer data across their systems. Organization disaster recovery policies also mandate backup policies to prevent data loss. Thus, the data in these databases are subject to a range of policies, including data retention and data purging rules, which may come into conflict with the need for regular backups. In this paper, we discuss the trade-offs between different compliance mechanisms to maintain IT Operational policies. We consider the practical availability of data in an active relational database and in a backup, including: 1) supporting data privacy rules with respect to preserving or purging customer data, and 2) the application performance impact caused by the database policy implementation. We first discuss the state of data privacy compliance in database systems. We then look at enforcement of common IT operational policies with regard to database backups. We consider different implementations used to enforce privacy rule compliance combined with a detailed discussion for how these approaches impact the performance of a database at different phases. We demonstrate that naive compliance implementations will incur a prohibitively high cost and impose onerous restrictions on backup and restore process, but will not affect daily user query transaction cost. However, we also show that other solutions can achieve a far lower backup and restore costs at a price of a small (<5%) overhead to non-SELECT queries. 

   more » « less
3. Forget About It: Batched Database Sanitization

   https://doi.org/10.1145/3605098.3636054  

   Wagner, James; Rasin, Alexander (April 2024, ACM)

   In file systems and database management systems (DBMSes), deleting data marks it as unallocated storage rather than explicitly erasing data. This data can be reconstructed from raw storage, making it vulnerable to data theft and exposing organizations to liability and compliance risks, violating data retention and destruction policies. The problem is further magnified in DBMSes because (unlike in file systems) DBMS backups are performed in pages and will include such deleted records. Data erasure (or sanitization) is a process that eliminates this vulnerability, providing users with “the right to be forgotten”. However, most of the work in data sanitization is only relevant to erasing data at the file system level, and not in DBMSes. Limited existing work in database sanitization takes an erase-on-commit approach, which can introduce significant I/O bottlenecks. In this paper, we describe a novel data sanitization method, DBSanitizer, that 1) is DBMS agnostic, 2) can batch value erasure, and 3) targets specific data to erase. DBSanitizer is designed as a template for DBMS vendors to support backup sanitization and ensure that no undesirable data is retained in backups. In this paper, we demonstrate how our approach can be used in any row-store relational DBMS (including Oracle, PostgreSQL, MySQL, and SQLite). As there are no backup sanitization tools available on the market or in research literature, we evaluate DBSanitizer, in a live database that supports erase-on-commit sanitization approach. 

   more » « less
4. Database Framework for Supporting Retention Policies

   https://doi.org/10.1007/978-3-030-86472-9_21  

   Scope, Nick; Rasin, Alexander; Wagner, James; Lenard, Ben; Heart, Karen (August 2021, International Conference on Database and Expert Systems Applications)

   Compliance with data retention laws and legislation is an important aspect of data management. As new laws governing personal data management are introduced (e.g., California Consumer Privacy Act enacted in 2020) and a greater emphasis is placed on enforcing data privacy law compliance, data retention support must be an inherent part of data management systems. However, relational databases do not currently offer functionality to enforce retention compliance. In this paper, we propose a framework that integrates data retention support into any relational database. Using SQL-based mechanisms, our system supports an intuitive definition of data retention policies. We demonstrate that our approach meets the legal requirements of retention and can be implemented to transparently guarantee compliance. Our framework streamlines compliance support without requiring database schema changes, while incurring an average 6.7% overhead compared to the current state-of-the-art solution. 

   more » « less
5. What Is Lurking in Your Backups?

   https://doi.org/10.1007/978-3-030-78120-0_26  

   Lenard, Ben; Rasin, Alexander; Scope, Nick; Wagner, James (June 2021, IFIP International Conference on ICT Systems Security and Privacy Protection)

   Best practices in data management and privacy mandate that old data must be irreversibly destroyed. However, due to performance optimization reasons, old (deleted or updated) data is not immediately purged from active database storage. Database backups that typically work by backing up table and index pages (rather than logical rows) greatly exacerbate the privacy problem of the old surviving data. Copying such deleted data into backups ensures that unknown quantities of old data can be stored indefinitely. In this paper, we quantify the amount of deleted data retained in backups by four major representative databases, comparing the default behavior versus an explicit defrag operation. We review the defrag options available in these databases and discuss the impact they have on eliminating old data from backups. We demonstrate that each database has a defrag mechanism that can eliminate most of old deleted data (although in Oracle pre-update content may survive defrag). Finally, we outline the factors that organizations should consider when deciding whether to apply defrag prior to executing their backups. 

   more » « less