Privacy Policy – User Uploaded Images on Content Sharing Sites

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 3, Issue 3, March 2016 Privacy Policy – User Uploaded Images on Content Sharing Sites [1] D.Akhila Reddy [2] K Suresh, [3] Prasad B [1] II/IV, [2][3]Associate Professor [1][2][3] Department of CSE, Marri Laxman Reddy Institute of Technology and Management (MLRITM) Hyderabad [1] [email protected] [2] [email protected] [3][email protected] Abstract With the increasing volume of images users share through social sites, maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users share personal information. In light of these incidents, the need of tools to help users control access to their shared content is apparent. Toward addressing this need, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. We examine the role of social context, image content, and metadata as possible indicators of users’ privacy preferences. We propose a two-level framework which determines the best available privacy policy for the user’s images being uploaded. Our solution relies on an image classification framework for image categories which may be associated 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. Keywords: Publicized Incidents, Shared Content, Adaptive Privacy Policy Prediction, Privacy Preferences. I. INTRODUCTION Images are now one of the key enablers of users’ connectivity. Sharing takes place both among previously established groups of known people or social circles (e. g., Google+, Flickr or Picasa), and also increasingly with people outside the users social circles, for purposes of social discovery-to help them identify new peers and learn about peers interests and social surroundings. However, semantically rich images may reveal content sensitive information []. Consider a photo of a students 2012 graduation ceremony, for example. It could be shared within a Google+ circle or Flickr group, but may unnecessarily expose the students BApos family members and other friends. Sharing images within online content sharing sites, there fore ,may quickly lead to unwanted disclosure and privacy violations [3], [24]. Further, the persistent nature of online media makes it possible for other users to collect rich aggregated information about the owner of the published content and the subjects in the published content [3], [20], [24]. The aggregated information can result in unexpected exposure of one’s social environment and lead to abuse of one’s personal information. Most content sharing websites allow users to enter their privacy preferences. Unfortunately, recent studies have shown that users struggle to set up and maintain such privacy settings [1], [11], [22], [33]. One of the main reasons provided is that given the amount of shared information this process can be tedious and errorprone. Therefore, many have acknowledged the need of policy recommendation systems which can assist users to easily and properly configure privacy settings [7], [22], [28], [30]. However, existing proposals for automating privacy settings appear to be inadequate to address the unique privacy needs of images [3], [5], [41], due to the amount of information implicitly carried within images, and their relationship with the online environment wherein they are exposed. In this paper, we propose an Adaptive Privacy Policy Prediction (A3P) system which aims to provide users a hassle free privacy settings experience by automatically generating personalized policies. The A3P system handles user uploaded images, and factors in the following criteria that influence one’s privacy settings of images: _ The impact of social environment and personal characteristics. Social context of users, such as their profile information and relationships with others may provide useful information regarding users’ privacy preferences. For example, users interested in photography may like to share their photos with other amateur photographers. Users who have several family members among their social contacts may All Rights Reserved © 2016 IJERCSE 514 International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 3, Issue 3, March 2016 share with them pictures related to family events. However, using common policies across all users or across users with similar traits may be too simplistic and not satisfy individual preferences. Users may have drastically different opinions even on the same type of images. For example, a privacy adverse person may be willing to share all his personal images while a more conservative person may just want to share personal images with his family members. recommendations for a user’s potential privacy improvement. We design the interaction flows between the two building blocks to balance the benefits from meeting personal characteristics and obtaining community advice. To assess the practical value of our approach, we built a system prototype and performed an extensive experimental evaluation. We collected and tested over 5,500 real policies generated by more than 160 users. Our experimental results demonstrate both efficiency and high prediction accuracy of our system. A preliminary discussion of the A3P-core was presented in [32]. In this work, we present an overhauled version of A3P, which includes an extended policy prediction algorithm in A3P-core (that is now parameterized based on user groups and also factors in possible outliers), and a new A3P-social module that develops the notion of social context to refine and extend the prediction power of our system. We also conduct additional experiments with a new data set collecting over 1,400 images and corresponding policies, and we extend our analysis of the empirical results to unveil more insights of our system’s performance. Fig 1 : System overview II. In light of these considerations, it is important to find the balancing point between the impact of social environment and users’ individual characteristics in order to predict the policies that match each individual’s needs. Moreover, individuals may change their overall attitude toward privacy as time passes. In order to develop a personalized policy recommendation system, such changes on privacy opinions should be carefully considered. The role of image’s content and metadata. In general, similar images often incur similar privacy preferences, especially when people appear in the images. For example, one may upload several photos of his kids and specify that only his family members are allowed to see these photos. He may upload some other photos of landscapes which he took as a hobby and for these photos, he may set privacy preference allowing anyone to view and comment the photos. Analyzing the visual content may not be sufficient to capture users’ privacy preferences. Tags and other metadata are indicative of the social context of the image, including where it was taken and why [4], and also provide a synthetic description of images, complementing the information obtained from visual content analysis. Corresponding to the aforementioned two criteria, the proposed A3P system is comprised of two main building blocks (as shown in Fig. 1): A3P-Social and A3P-Core. The A3P-core focuses on analyzing each individual user’s own images and metadata, while the A3P-Social offers a community perspective of privacy setting SYSTEM ANALYSIS Existing System: Most content sharing websites allow users to enter their privacy preferences. Unfortunately, recent studies have shown that users struggle to set up and maintain such privacy settings. One of the main reasons provided is that given the amount of shared information this process can be tedious and errorprone. Therefore, many have acknowledged the need of policy recommendation systems which can assist users to easily and properly configure privacy settings. Disadvantages of Existing System: Sharing images within online content sharing sites,therefore,may quickly leadto unwanted disclosure and privacy violations. Further, the persistent nature of online media makes it possible for other users to collect rich aggregated information about the owner of the published content and the subjects in the published content. The aggregated information can result in unexpected exposure of one’s social environment and lead to abuse of one’s personal information. Proposed System: In this paper, we propose an Adaptive Privacy Policy Prediction (A3P) system which aims to provide users All Rights Reserved © 2016 IJERCSE 515 International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 3, Issue 3, March 2016 a hassle free privacy settings experience by automatically generating personalized policies. The A3P system handles user uploaded images, and factors in the following criteria that influence one’s privacy settings of images: The impact of social environment and personal characteristics. Social context of users, such as their profile information and relationships with others may provide useful information regarding users’ privacy preferences. For example, users interested in photography may like to share their photos with other amateur photographers. The role of image’s content and metadata. In general, similar images often incur similar privacy preferences, especially when people appear in the images. For example, one may upload several photos of his kids and specify that only his family members are allowed to see these photos. Admin: Advantages of Proposed System: The A3P-core focuses on analyzing each individual user’s own images and metadata, while the A3PSocial offers a community perspective of privacy setting recommendations for a user’s potential privacy improvement. We design the interaction flows between the two building blocks to balance the benefits from meeting personal characteristics and obtaining community advice. Fig 3 : Flow chart for user B. III. SYSTEM DESIGN A . Flow Chart: User: Data Flow Diagrams: Fig 4: Data Flow Diagrams C. System Architecture: System Admin: The system admin is responsible for providing authorization for a specified users and can do some operations such as view uploaded images, view the searching history, view all image ranking and view all users, search images and logout. Fig 2 : Flow chart for user Upload Images : In this module, the user can upload n number of images by their policies If user want to upload new image then he has enter some fields like All Rights Reserved © 2016 IJERCSE 516 International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 3, Issue 3, March 2016 image name, image color, image description, image type, image usage, browse the image file and upload. After uploading successfully he will get a response from the server. Initially new uploaded image rank is zero. After viewing that image rank will re-rank. End User: In this module, there are n numbers of users are present. User should register before doing some operations. And register user details are stored in user module. After registration successful he has to login by using authorized user name and password. Login successful he will do some operations like view my details, search images, request secrete key and logout. The user click on my details link then the server will give response to the user with all details such as user name, phone no, address, e mail ID and location. Before searching any images user should request a authorization to admin, then the admin will provide an authorization for particular user and send to the user. After getting an authorization user can search the images base on query or keyword and field like image name, image color, and image usage and image type. And server will give response to the user, then that image rank will be increased. Such a hierarchical classification gives a higher priority to image content and minimizes the influence of missing tags. Note that it is possible that some images are included in multiple categories as long as they contain the typical content features or metadata of those categories. Re-ranking chart : In this module, we can view the image ranking chart for all the images. This chart shows the ranking images in the form of PI diagram with the image name and image color. After viewing the images, rank will be increased and the re-ranking Pi diagram chart will increased based on the number of views. D. MODULES The modules involved in the system are System Construction Module, Content-Based Classification, Metadata-Based Classification and Adaptive Policy Prediction. System Construction Module: The A3P system consists of two main components: A3P-core and A3P-social. The overall data flow is the following. When a user uploads an image, the image will be first sent to the A3P-core. The A3Pcore classifies the image and determines whether there is a need to invoke the A3P-social. In most cases, the A3P-core predicts policies for the users directly based on their historical behavior. If one of the following two cases is verified true, A3P-core will invoke A3Psocial: (i) The user does not have enough data for the type of the uploaded image to conduct policy prediction; (ii) The A3P-core detects the recent major changes among the user’s community about their privacy practices along with user’s increase of social networking activities (addition of new friends, new posts on one’s profile etc). Content-Based Classification: Fig 5 : System Architecture Content-Based Classification: To obtain groups of images that may be associated with similar privacy preferences, we propose a hierarchical image classification which classifies images first based on their contents and then refine each category into subcategories based on their metadata. Images that do not have metadata will be grouped only by content. To obtain groups of images that may be associated with similar privacy preferences, we propose a hierarchical image classification which classifies images first based on their contents and then refine each category into subcategories based on their metadata. Images that do not have metadata will be grouped only by content. Such a hierarchical classification gives a higher priority to image content and minimizes the influence of missing tags. Note that it is possible that some images are included in multiple categories as long as they contain the typical content features or metadata of those categories. Our All Rights Reserved © 2016 IJERCSE 517 International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 3, Issue 3, March 2016 approach to content-based classification is based on an efficient and yet accurate image similarity approach. Specifically, our classification algorithm compares image signatures defined based on quantified and sanitized version of Haar wavelet transformation. For each image, the wavelet transform encodes frequency and spatial information related to image color, size, invariant transform, shape, texture, symmetry, etc. Then, a small number of coefficients are selected to form the signature of the image. The content similarity among images is then determined by the distance among their image signatures. Fig 6 shows window allows a new user to view the home page. Fig 7 : Admin login page. Metadata-Based Classification: The metadata-based classification groups images into subcategories under aforementioned baseline categories. The process consists of three main steps. The first step is to extract keywords from the metadata associated with an image. The metadata considered in our work are tags, captions, and comments. The second step is to derive a representative hypernym (denoted as h) from each metadata vector. The third step is to find a subcategory that an image belongs to. This is an incremental procedure. At the beginning, the first image forms a subcategory as itself and the representative hypernyms of the image becomes the subcategory’s representative hypernyms. Fig 7 window helps a new user to log in into admin page. Fig 8 : Admin menu page. Fig 8 window helps admin to add image, view all images policy, ranking, image details. Adaptive Policy Prediction: The policy prediction algorithm provides a predicted policy of a newly uploaded image to the user for his/her reference. More importantly, the predicted policy will reflect the possible changes of a user’s privacy concerns. The prediction process consists of three main phases: (i) policy normalization; (ii) policy mining; and (iii) policy prediction. IV. SYSTEM IMPLEMENTATION Fig 9 : Registration page Fig 9 window helps a new user to register in the page. Fig 6 : Welcome page. All Rights Reserved © 2016 IJERCSE 518 International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 3, Issue 3, March 2016 significant improvements over current approaches to privacy. REFERENCES [1] 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. Fig 10: User details. Fig 10 window helps us to view User Details. [2] 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. [3] 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. [4] M. Ames and M. Naaman, “Why we tag: Motivations for annotation in mobile and online media,” in Proc. Conf. Human Factors Comput. Syst., 2007, pp. 971–980. Fig 11: List of users Fig 11 window helps to view the List of users. [5] 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. [6] D. G. Altman and J. M. Bland ,“Multiple significance tests: The bonferroni method,” Brit. Med. J., vol. 310, no. 6973, 1995. [7] J. Bonneau, J. Anderson, and L. Church, “Privacy suites: Shared privacy for social networks,” in Proc. Symp. Usable Privacy Security, 2009. Fig 12 : Content based searching images This fig 12 window helps Users to search images based on content. V. CONCLUSION We have proposed an Adaptive Privacy Policy Prediction (A3P) system that helps users automate the privacy policy settings for their uploaded images. 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All Rights Reserved © 2016 IJERCSE 519 International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 3, Issue 3, March 2016 [11] 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. [12] R. da Silva Torres and A. Falc~ao, “Contentbased image retrieval: Theory and applications,” Revista de Inform_atica Te_orica e Aplicada, vol. 2, no. 13, pp. 161–185, 2006. All Rights Reserved © 2016 IJERCSE 520