1. The document discusses using active learning techniques like uncertainty sampling to create privacy preferences for sharing data on social networks. 2. It involves asking users questions about their relationships to friends and commonalities to group friends and determine what profile features to share. 3. A decision tree algorithm like CART is used to map friend groups and commonalities to whether profile features like date of birth should be shared or not shared with each group.

1. Approaches to ML Techniques on
Real World Data
A Demo on Behavioral Analysis in
Social Networking Sites.
--Venkata Ramana C

2. Real World Data
Social Networking Sites
Blogs
Forums
Tweets …

4. Aim
• Create an Environment where you create your
own rules on how to share your data on the
Web.

5. Technique
• Active Learning

6. Active Learning
• The key idea behind active learning is that a machine learning
algorithm can achieve greater accuracy with fewer labelled
training instances if it is allowed to choose the data from
which is learns.
• An active learner may ask queries in the form of unlabeled
instances to be labelled by an oracle (e.g., a human
annotator).

7. Scenario

8. Uncertainty Sampling
• The technique of the project is as follows:
• there will be questions to the user about each friend as below,
followed by what the user wants to share with his friend -- which are the
profile features.
• How are u associated with your friend? ( or )
• What do u have in common with your friend?
• 1.personal
• 2.donno how (shall take some time to decide ?)
• 3.we have ...x.y.z....(specify) in common. [this will form a group]

9. Pseudo Algorithm
• Input: initial small training set L, and pool of unlabeled
data set U
Use L to train the initial classifier C
– Repeat
• Use the current classifier C to label all unlabeled
examples in U.
• Use uncertainty sampling technique to select
m2 most informative unlabeled examples, and
ask oracle H for labelling.
• Augment L with these m new examples, and
remove them from U.
• Use L to retrain the current classifier C.
Until the predefined stopping criterion SC is met.

10. So When is This Useful?

11. Friend Groups

12. Active Learning for Privacy
Courtesy: Privacy Wizards for Social Networking Sites. WWW2010

13. Concepts
• Gini Impurity
- the expected error rate.
• Entropy
- how mixed up a set is?
p(i) = frequency(outcome) = count(outcome) / count(total rows)
Entropy = sum of p(i) x log(p(i)) for all outcomes

14. Courtesy: Collective Intelligence by Toby Segaran

15. CART
(Classification and Regression Trees)
• Decision tree classifiers are simple to view and
interpret.
• If-Then rules.

16. Application Data.
• Id group1 group2 ..... DOB
• 123 Yes No ..... Share
• 124 No Yes ..... NotShare
• ...... ... ..... .....
• ...... ... ..... .....
• ...... ... ..... .....
• ...... ... ..... .....
• 129 Yes Yes ..... Share

17. Demo
• We will use Decision Trees to train some of
our friends in Social Networks to set Privacy
Preferences.

18. Demo Results:
• [u'project', u'srm', u'sssg'] => Feature Vector
[0, 1, 2] => mapped by CART algorithm
• Goal is to ‘Share’ or ‘NotShare’
Dob, zip, religion, phone, email.
For Dob:
*‘yes’, ‘No’, ‘No’, ‘Share’+ (from Vasu)
*‘No’, ‘Yes’, ‘No’, ‘NotShare’+ (from Cigith)
*‘No’, ‘No’, ‘Yes’, ‘Share’+ (from Harish)

19. Decision Tree for DOB:
0: Yes?
F/ T
,‘Share’: 2- ,‘NotShare’: 1-
Zip Code:
• [['Yes', 'No', 'No', 'Share'], ['No', 'Yes', 'No',
'NotShare'], ['No', 'No', 'Yes', 'Share']]
1:Yes?
T-> F->
{'NotShare': 1} {'Share': 2}

22. References
• Collective Intelligence by Toby Segaran.
• Privacy Wizards for Social Networking Sites.
WWW2010.