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Simplifying Data Disclosure Configurations in a Cloud Computing Environment

Published: 30 April 2015 Publication History

Abstract

Cloud computing offers a compelling vision of computation, enabling an unprecedented level of data distribution and sharing. Beyond improving the computing infrastructure, cloud computing enables a higher level of interoperability between information systems, simplifying tasks such as sharing documents between coworkers or enabling collaboration between an organization and its suppliers. While these abilities may result in significant benefits to users and organizations, they also present privacy challenges due to unwanted exposure of sensitive information. As information-sharing processes in cloud computing are complex and domain specific, configuring these processes can be an overwhelming and burdensome task for users. This article investigates the feasibility of configuring sharing processes through a small and representative set of canonical configuration options. For this purpose, we present a generic method, named SCON-UP (Simplified CON-figuration of User Preferences). SCON-UP simplifies configuration interfaces by using a clustering algorithm that analyzes a massive set of sharing preferences and condenses them into a small number of discrete disclosure levels. Thus, the user is provided with a usable configuration model while guaranteeing adequate privacy control. We describe the algorithm and empirically evaluate our model using data collected in two user studies (n = 121 and n = 352). Our results show that when provided with three canonical configuration options, on average, 82% of the population can be covered by at least one option. We exemplify the feasibility of discretizing sharing levels and discuss the tradeoff between coverage and simplicity in discrete configuration options.

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Published In

cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 6, Issue 3
Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence
May 2015
319 pages
ISSN:2157-6904
EISSN:2157-6912
DOI:10.1145/2764959
  • Editor:
  • Huan Liu
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2015
Accepted: 01 September 2014
Revised: 01 October 2013
Received: 01 August 2013
Published in TIST Volume 6, Issue 3

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Author Tags

  1. Privacy
  2. artificial intelligence (AI)
  3. cloud computing
  4. data protection
  5. human--computer interaction
  6. information disclosure
  7. intelligent agents
  8. preference clustering

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Cited By

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  • (2022)Harnessing Soft Logic to Represent the Privacy ParadoxInformatics10.3390/informatics90300549:3(54)Online publication date: 18-Jul-2022
  • (2019)Research on abnormal data detection method of web browser in cloud computing environmentCluster Computing10.1007/s10586-017-1221-922:1(1229-1238)Online publication date: 1-Jan-2019
  • (2017)Analyzing and Optimizing Access Control Choice Architectures in Online Social NetworksACM Transactions on Intelligent Systems and Technology10.1145/30466768:4(1-22)Online publication date: 11-May-2017
  • (2016)Resolving Multi-Party Privacy Conflicts in Social MediaIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.253916528:7(1851-1863)Online publication date: 1-Jul-2016

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