 Approaches to Enforce Privacy in Databases: Classical to Information Flow-Based ModelsR.K. Shyamasundar (),
Pratiksha Chaudhary (),
Arushi Jaiswal () and
Aniket Kuiri ()
Information Systems Frontiers, 2021, vol. 23, issue 4, No 2, 833 pages Abstract: Abstract Ever since databases became an ubiquitous part of enterprises or businesses, security and privacy became a requirement. Traditionally, privacy was realized through various methods of database access control and relied much on the use of statically defined views, which are essentially logical constructs imposed over database tables that can alter or restrict the data that can be viewed by an user. Privacy is about the responsible maintenance of private information. This responsibility is hard to define, which is why laws are necessary. With a vast accumulation of personal data in databases, there has been a heightened awareness and concern about the storage and use of private information leading to privacy-related guidelines, regulations and legislations, Compliance with these regulations has become one of the major concerns for organizations and companies. Traditionally, privacy in databases (DBs) have been addressed through access control techniques including multi-level security (MLS) based on mandatory access control (MAC), and restricted views to the users. As view definitions to comply with regulations became quite complex for accommodating all the restrictions in one view, explicit constructs for specifying privacy policies were introduced for complying with medical regulations like HIPAA (Health Insurance Portability and Accountability Act) from USA, in relational database systems. These enabled fine grained access control (FGAC) capable of enforcing disclosure control enunciated databases. Application of information flow control that is needed for multi-level security (MLS) databases to preserve privacy among multiple users but have their challenges like new abstractions for managing information flow in a relational database system, handling transactions and integrity constraints without introducing covert channels etc. As the DBs need to work alongside information flow controlled programming languages and operating systems for tracking flows, there is a need to enforce the security policy not only on the DBMS but also on the application platform. Due to the underlying requirement of decentralization, it calls for declassification/endorsement and santization requirements on the DB. In this paper, we shall first review some of the major privacy enhancing techniques used traditionally for DBs including MLS DBs, and then explore application of decentralized information flow control models for realizing information flow secure DBs in a robust manner. Towards the end, we shall also touch upon some of the roles of anonymization and psuedonymization including inference control and differential privacy in realizing privacy in practice. Keywords: Privacy; Security; Information flow; Declassification; Relational database (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:infosf:v:23:y:2021:i:4:d:10.1007_s10796-021-10178-w Ordering information: This journal article can be ordered from DOI: 10.1007/s10796-021-10178-w Access Statistics for this article Information Systems Frontiers is currently edited by Ram Ramesh and Raghav Rao More articles in Information Systems Frontiers from Springer |
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