![](bga.png)
interactive since it involves the user in the decision-
making process. The latter can also query the
ontology to obtain the necessary knowledge. Securing
data by anonymization and preserving an intended
quality are usually contradictory objectives.
Therefore, the anonymization process, implemented
in MAGGO, aims at a trade-off between these
objectives, depending on the usage requirement of the
anonymized data. Our approach is currently limited
to anonymization of microdata sets by generalization.
However, we have endeavored to make it as generic
as possible so that it can be applied to other microdata
anonymization techniques. Finally, to promote its
evolution and its incremental implementation, we
opted for a model driven approach. OPAM was
published in a previous paper. The contribution of this
paper is twofold: i) a meta-model to describe the
different components of the approach, ii) the
methodology MAGGO which performs the whole
anonymization process. Moreover, we illustrate the
contributions with an example and describe a
controlled experiment conducted to validate the
added value of the approach. There are two main
avenues for future work. First, we want to conduct an
experiment on a larger scale including users that have
low skills in computer science in order to obtain a
stronger evaluation of MAGGO. This will allow us to
confirm the usability of our approach and tool.
Second, we want to perform the same effort to extend
MAGGO to other micro-data anonymization
techniques.
REFERENCES
BenFredj, F., Lammari, N., Comyn-Wattiau, I., 2015.
Building an Ontology to Capitalize and Share
Knowledge on Anonymization Techniques. In ECKM
2015, 16th European Conference on Knowledge
Management, pp 122-131. Edited by Massaro, M. &
Garlatti, A., ISBN: 978-1-910810-46-0.
BenFredj, F., Lammari, N., Comyn-Wattiau, I., 2014.
Characterizing Generalization Algorithms-First
Guidelines for Data Publishers, In KMIS 2014,
International Conference on Knowledge Management
and Information Sharing, pp 360-366. SciTePress
Science and Technology Publications. ISBN: 978-989-
758-050-5.
Brand, R., 2002. Microdata Protection through Noise
Addition, In Inference Control in Statistical Databases-
From Theory to Practice. Domingo-Ferrer (Ed.), pp
97-116. Springer.
Dai, C., Ghinita, G., Bertino, E., Byun, J., Li, N.2009.
TIAMAT: a Tool for Interactive Analysis of Microdata
Anonymization Techniques, In VLDB’09, Vol 2(2),
1618-1621.
Defays, D., Nanopoulos, P., 1993. Panels of Enterprises and
Confidentiality: the Small Aggregates Method, In 92nd
Symposium on Design and Analysis of Longitudinal
Surveys, pp 195-204, Ontorio, Canada.
Fienberg, S.E., McIntyre, J., 2004. Data swapping:
Variations on a theme by dalenius and reiss, In PSD
2004, Privacy in statistical databases, LNCS 3050, pp.
14-29. Domingo-Ferrer & Torra (Eds.), Springer.
Fung, B. C. M., Wang, K., Chen, R., Yu, P. S., 2010.
Privacy Preserving Data Publishing-a survey of recent
developments, In ACM Comput. Survey, Vol. 42(4), pp
14:1-14:53.
Ilavarasi, B., Sathiyabhama, A. K., Poorani, S., 2013. A
survey on privacy preserving data mining techniques,
In IJCSBI journal, 7(1), ISSN: 1694, pp 209-221.
Loh, W-Y., 2011. Classification and regression trees, In
Wiley Interdisc. Rew.: Data Mining and Knowledge
Discovery, Vol 1(1), pp 14-23.
Madan, A., Dubey, S. K., 2012. Usability Evaluation
Methods: a Literature Review. In IJEST journal, ISSN
0975-5462, Vol 4(2).
Patel, L., Gupta, R., 2013. A Survey of Perturbation
Technique for Privacy-Preserving of Data, In IJTAE
journal, Vol 3(6), pp 162-166, ISSN 2250-2459.
Poulis, G., Gkoulalas-Divanis, A., Loukides, G.,
Skiadopoulos, S., Tryfonopoulos, C., 2015. SECRETA:
A System for Evaluating and Comparing Relational and
Transaction Anonymization algorithms, In Medical
Data Privacy Handbook, Chapter 4, Springer Int.
Publishing, pp.83-109.
Saaty, T.L, Sodenkamp, M.A., 2008. Making decisions in
hierarchic and network systems, In IJADS journal,
ISSN 1755-8077, Vol 1(1), pp 24-79.
Samarati, P., 2001. Protecting respondents’ identities in
microdata release, In IEEE Trans. on Knowl. and Data
Eng., Vol 13(6), pp 1010-1027.
Silver, M. S., 2006. Broadening the Scope. Human-
Computer Interaction and Management Information
Systems: Foundations, 90.
Sweeney, L., 2002. k-Anonymity: A model for Protecting
Privacy, Int. Journal of Uncertainty, Fuzziness and
Knowledge-Based Systems, Vol 10(5), pp 557-570.
Xiao, X., Wang, G., Gehrke, G., 2009. Interactive
Anonymization of Sensitive Data, In SIGMOD’09,
Binnig C. & Dageville B.(Eds.), pp 1051–1054, New
York, USA.
ICISSP 2018 - 4th International Conference on Information Systems Security and Privacy
170