Project topics * Private data management

Project topics – Private data
Nov. 2011
Topic 1: Survey on the Status of Privacy Specifications in
Online Social Networks
• Study at least 8 online social networks (OSNs) (including
Facebook, LinkedIn, Google+ and Flickr) and report of how
each one of them handles privacy specifications.
• The output of this study is expected to be:
– A characterization of what a user is allowed to specify in terms of "what piece
of information" (e.g., photo, wall post, status update, etc) is visible to "what
type of users" (e.g., friends, friends-of-friends, lists, etc) and what is the
default setting.
As an example of the expected output of this study consider Table 1 in [1] but more detailed (*)
– A ranking of the 8 OSNs with regards to "how much" private these OSN are,
using one or more appropriate metrics, for example, using ideas from [2]
Read [3] for some nice ideas on how to improve the current situation.
[1] Barbara Carminati, Elena Ferrari, Andrea Perego: Enforcing access control in Web-based social networks. ACM Trans. Inf.
Syst. Secur. 13(1): (2009)
[2] Kun Liu, Evimaria Terzi: A Framework for Computing the Privacy Scores of Users in Online Social Networks. TKDD 5(1): 6
[3] Krishna P. Gummadi, Alan Mislove, and Balachander Krishnamurthy. Addressing the Privacy Management Crisis in Online
Social Networks. In The IAB Workshop on Internet Privacy, December 2010. (Position Paper)
Topic 1: Survey on the Status of Privacy Specifications in
Online Social Networks
Table 1 of [1]
Topic 2: Experimental Evaluation of the Privacy of a Real
• Choose 2 real data sets from OSNs (or 2 different
subsets of the same data set)
• Build the corresponding social network graphs.
Check the web page for some links of where to get datasets.
• Evaluate the resulting graphs in terms of
– (1) k-degree anonymity [4], and
– (2) an additional k-anonymity based criteria of your
[4] Kun Liu, Evimaria Terzi: Towards identity anonymization on graphs.
Local recoding with hierarchies
• How do we anonymize a table with categorical
attributes in the QI set,
– with local recoding +
– with hierarchies playing a role in the process?
• Implement+test the KACA algorithm
• Jiuyong Li, Raymond Chi-Wing Wong, Ada WaiChee Fu, Jian Pei. Anonymization by Local
Recoding in Data with Attribute Hierarchical
Taxonomies. IEEE Trans. Knowl. Data Eng. 20(9):
1181-1194 (2008)
Local recoding with hierarchies (2)
• Another approach on the topic:
– “Cut-off” a single ancestor value per detailed
• Implement + test the proposed algorithm
• Junqiang Liu, Ke Wang. On Optimal
Anonymization for L(+)-Diversity. Proceedings
of 26th IEEE International Conference on Data
Engineering, March 1-6, 2010, Long Beach,
California, USA
• Do sth with existing toolkits (Cornell, Udallas)
– Port Cornell’s toolkit to MySQL / generic DB ?
– Port Udallas to java ?
• Convert UoI code to toolkit?

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