Enhancing data utility in differential privacy via microaggregation-based $$k$$ k -anonymity

Enhancing data utility in differential privacy via microaggregation-based $$k$$ k -anonymity It is not uncommon in the data anonymization literature to oppose the “old” $$k$$ k -anonymity model to the “new” differential privacy model, which offers more robust privacy guarantees. Yet, it is often disregarded that the utility of the anonymized results provided by differential privacy is quite limited, due to the amount of noise that needs to be added to the output, or because utility can only be guaranteed for a restricted type of queries. This is in contrast with $$k$$ k -anonymity mechanisms, which make no assumptions on the uses of anonymized data while focusing on preserving data utility from a general perspective. In this paper, we show that a synergy between differential privacy and $$k$$ k -anonymity can be found: $$k$$ k -anonymity can help improving the utility of differentially private responses to arbitrary queries. We devote special attention to the utility improvement of differentially private published data sets. Specifically, we show that the amount of noise required to fulfill $$\varepsilon $$ ε -differential privacy can be reduced if noise is added to a $$k$$ k -anonymous version of the data set, where $$k$$ k -anonymity is reached through a specially designed microaggregation of all attributes. As a result of noise reduction, the general analytical utility of the anonymized output is increased. The theoretical benefits of our proposal are illustrated in a practical setting with an empirical evaluation on three data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Enhancing data utility in differential privacy via microaggregation-based $$k$$ k -anonymity

Loading next page...
 
/lp/springer_journal/enhancing-data-utility-in-differential-privacy-via-microaggregation-SAOHZFOJJc
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2014 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-014-0351-4
Publisher site
See Article on Publisher Site

References

  • On syntactic anonymity and differential privacy
    Clifton, C; Tassa, T
  • Ordinal, continuous and heterogeneous $$k$$ k -anonymity through microaggregation
    Domingo-Ferrer, J; Torra, V
  • Efficient multivariate data-oriented microaggregation
    Domingo-Ferrer, J; Martínez-Ballesté, A; Mateo-Sanz, J; Sebé, F
  • A firm foundation for private data analysis
    Dwork, C

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial