An Adaptive Privacy Policy Prediction for User Uploading Images and Shared Data

. Yadagiri, G. Dayakar, Shaik Abdul Nabi


An Adaptive Privacy Policy Prediction (A3P) system to support users to comprise privacy settings for their images. With the accumulative volume of images, user’s stake through social sites, sustaining privacy has become a major problem, as proven by a recent trend of publicized happenings where users unintentionally shared personal information. In such a case of incidents, the need of tools to help users control access to their shared content is superficial. Towards addressing this need, it is examined the role of social context, image content, and metadata as probable indicators of users’ privacy partialities. A two-level framework which is rendering to the user’s available history on the site, defines the best available privacy policy for the user’s images being uploaded. The solution depend on an image classification framework for image categories which may be accompanied with similar policies, and on a policy prediction algorithm to automatically generate a policy for each newly uploaded image, also according to users’ social features. Over time, the created policies will follow the evolution of users’ privacy attitude. It also provides the results of extensive evaluation which determine the efficacy of the system, with prediction accuracies.

Full Text:



A. Acquisti and R. Gross, “Imagined communities: Awareness, information sharing, and privacy on the facebook,” in Proc. 6th Int. Conf. Privacy Enhancing Technol. Workshop, 2006, pp. 36–58.

R. Agrawal and R. Srikant,“Fast algorithms for mining association rules in large databases,” in Proc. 20th Int. Conf. Very Large Data Bases, 1994, pp. 487–499.

S. Ahern, D. Eckles, N. S. Good, S. King, M. Naaman, and R. Nair, “Over-exposed?: Privacy patterns and considerations in online and mobile photo sharing,” in Proc. Conf. Human Factors Comput. Syst., 2007, pp. 357–366.

M. Ames and M. Naaman, “Why it is tag: Motivations for annotation in mobile and online media,” in Proc. Conf. Human Factors Comput. Syst., 2007, pp. 971–980.

A. Besmer and H. Lipford, “Tagged photos: Concerns, perceptions, and protections,” in Proc. 27th Int. Conf. Extended Abstracts Human Factors Comput. Syst., 2009, pp. 4585–4590.

D. G. Altman and J. M. Bland ,“Multiple significance tests: The bonferroni method,” Brit. Med. J., vol. 310, no. 6973, 1995.

J. Bonneau, J. Anderson, and L. Church, “Privacy suites: Shared privacy for social networks,” in Proc. Symp. Usable Privacy Security, 2009.

J. Bonneau, J. Anderson, and G. Danezis, “Prying data out of a social network,” in Proc. Int. Conf. Adv. Soc. Netw. Anal. Mining., 2009, pp.249–254.

H.-M. Chen, M.-H. Chang, P.-C. Chang, M.-C. Tien, W. H. Hsu, and J.-L. Wu, “Sheepdog: Group and tag recommendation for flickr photos by automatic search-based learning,” in Proc. 16th ACM Int. Conf. Multimedia, 2008, pp. 737–740.

M. D. Choudhury, H. Sundaram, Y.-R. Lin, A. John, and D. D. Seligmann, “Connecting content to community in social media via image content, user tags and user communication,” in Proc. IEEE Int. Conf. Multimedia Expo, 2009, pp.1238–1241.

L. Church, J. Anderson, J. Bonneau, and F. Stajano, “Privacy stories: Confidence on privacy behaviors through end user programming,” in Proc. 5th Symp. Usable Privacy Security, 2009.

R. da Silva Torres and A. Falc~ao, “Content-based image retrieval: Theory and applications,” Revista de Inform_atica Te_orica e Aplicada, vol. 2, no. 13, pp. 161–185, 2006.

R. Datta, D. Joshi, J. Li, and J. Wang, “Image retrieval: Ideas, influences, and trends of the new age,” ACM Comput. Surv., vol. 40, no. 2, p. 5, 2008.

A. Mazzia, K. LeFevre, and A. E.,, “The PViz comprehension tool for social network privacy settings,” in Proc. Symp. Usable Privacy Security, 2012.

K. Strater and H. Lipford, “Strategies and struggles with privacy in an online social networking community,” in Proc. Brit. Comput. Soc. Conf. Human-Comput. Interact., 2008, pp.111–119.

X. Su and T. M. Khoshgoftaar, “A survey of collaborative filtering techniques,” Adv. Artif. Intell., vol. 2009, p. 4, 2009.

X. Sun, H. Yao, R. Ji, and S. Liu, “Photo assessment based on computational visual attention model,” in Proc. 17th ACM Int. Conf. Multimedia, 2009, pp. 541–544.



  • There are currently no refbacks.

© International Journals of Advanced Research in Computer Science and Software Engineering (IJARCSSE)| All Rights Reserved | Powered by Advance Academic Publisher.