Project topics – Private data management

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 (2010) • [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 OSN

• 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 choice.

[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 Wai-

Chee Fu, Jian Pei. Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies. IEEE Trans. Knowl. Data Eng. 20(9): 1181-1194 (2008)

6

Local recoding with hierarchies (2)

• Another approach on the topic:– “Cut-off” a single ancestor value per detailed

value• 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

Toolkits

• Do sth with existing toolkits (Cornell, Udallas)– Port Cornell’s toolkit to MySQL / generic DB ?– Port Udallas to java ?

• Convert UoI code to toolkit?