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Computing Aggregates Over Numeric Data with Personalized Local Differential Privacy

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Information Security and Privacy (ACISP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10343))

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Abstract

The advancement of technology and the widespread usage of smart phones have made the collection of data from users easy and cost-effective, which allows the government, urban planner, and researchers to envision novel analysis. Along with the benefits, the shared data can bring serious privacy concerns as they reveal sensitive information about a user. Differential privacy has become an effective model for sharing privacy protected data with others. To facilitate users to protect the privacy of data before it leaves their personal devices, the concept of personal local differential privacy (PLDP) has been introduced for counting queries. We formulate PLDP for computing aggregates over numeric data. We present an efficient approach, private estimation of numeric aggregates (PENA), that guarantees PLDP of numeric data while computing an aggregate (e.g., the average or the minimum). We perform extensive experiments over a real dataset to show the effectiveness of PENA.

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Notes

  1. 1.

    https://www.waze.com.

  2. 2.

    http://money.cnn.com/galleries/2010/technology/1012/gallery.5_data_breaches.

  3. 3.

    https://en.wikipedia.org/wiki/AOL_search_data_leak.

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Acknowledgments

This research has been done in the department of Computer Science and Engineering, Bangladesh University of Engineering and Technology (BUET). The work is supported by BUET and United International University (UIU).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

    Mousumi Akter & Tanzima Hashem

Authors
  1. Mousumi Akter
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  2. Tanzima Hashem
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Corresponding author

Correspondence to Mousumi Akter .

Editor information

Editors and Affiliations

  1. Queensland University of Technology, Brisbane, Queensland, Australia

    Josef Pieprzyk

  2. Queensland University of Technology, Brisbane, Queensland, Australia

    Suriadi Suriadi

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Akter, M., Hashem, T. (2017). Computing Aggregates Over Numeric Data with Personalized Local Differential Privacy. In: Pieprzyk, J., Suriadi, S. (eds) Information Security and Privacy. ACISP 2017. Lecture Notes in Computer Science(), vol 10343. Springer, Cham. https://doi.org/10.1007/978-3-319-59870-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-59870-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59869-7

  • Online ISBN: 978-3-319-59870-3

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