Protecting Private Information by Data Separation in Distributed Spatial Data Warehouse

Gorawski, Marcin; Bularz, Jakub

doi:10.1109/ares.2007.118

Cited by 11 publications

(3 citation statements)

References 3 publications

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“…They proposed a new privacy method that migrates the data containing personally identifying information into an anonymized version of the data. Another approach is to store the separate pieces of composite personal data (in other words, related parts of a relational database) in different nodes of the data warehouse [26]. Narayanan, Madria, and St. Clair [27] suggested a system that may provide either exact or approximate answers to queries, depending on the user's permissions; this approach protects privacy by disallowing access to confidential information.…”

Section: "Legitimate" Aggregation: Data Miningmentioning

confidence: 99%

The Digital Aggregated Self: A Literature Review

Williams

Neal

2012

2012 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

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As the Internet rapidly establishes itself as a major communications conduit, growing concern exists about personal privacy issues and the related ownership of personal data. Privacy and personal data may be vulnerable to exposure by unauthorized individuals, by commercial entities wishing to profit from the data, and even by the individual to whom the data pertains. Although fragments of data may not present a privacy issue on their own, data mining and other aggregation methods quickly assemble data to create a considerably more sensitive "whole."This article presents an examination of aggregated personal data ownership, or "the digital aggregated self," using a literature review and an ethical argument. We propose that while server owners may possess the disaggregated user data stored on their servers, individuals should hold the rights to their set of aggregated data that is stored throughout the entire network of online servers.

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Section: "Legitimate" Aggregation: Data Miningmentioning

confidence: 99%

The Digital Aggregated Self: A Literature Review

Williams

Neal

2012

2012 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

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“…Data Separation: It is crucial to logically and physically separate personal data into multiple databases and on different servers to avoid linkability and prevent identification of an individual [12]. It is important to design database schema in such a way that the risky attribute combinations are separated which could otherwise lead to identification of linkages and ultimately identifiability.…”

Section: Privacy Aware Design Strategiesmentioning

confidence: 99%

Utilizing Shannon's Entropy to Create Privacy Aware Architectures

Palia¹,

Tandon²,

Mathis³

2021

Preprint

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Privacy is an individual's choice to determine which personal details can be collected, used and shared. Individual consent and transparency are the core tenets for earning customers' trust and this motivates the organizations to adopt privacy enhancing practices while creating the systems. The goal of a privacy-aware design is to protect information in a way that does not increase an adversary's existing knowledge about an individual beyond what is permissible. This becomes critical when these data elements can be linked with the wealth of auxiliary information available outside the system to identify an individual. Privacy regulations around the world provide directives to protect individual privacy but are generally complex and vague, making their translation into actionable and technical privacy-friendly architectures challenging. In this paper, we utilize Shannon's Entropy (SE) to create an objective metric that can help simplify the state-of-the-art Privacy Design Strategies proposed in the literature and aid our key technical design decisions to create privacy aware architectures.

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“…No solution individually can support all the needs for both privacy and data mining. In large organization data may be distributed in many databases and thus the privacy in such an environment may demand a different approach, such as relational decomposition, as prescribed by Marcin and Jakub [13]. To overcome the discovery of intrusion technique reconstructing back the private information from the randomized data tuples, for a distributed environment, Zang et al [14] have proposed an algebraic-technique-based scheme.…”

Section: Preserving Data Privacymentioning

confidence: 99%