Librarians as Feisty Advocates for Privacy

Librarians are the ideal professional group to advocate for privacy and intellectual freedom during online social media product use. Under the central leadership of the American Library Association (ALA), librarians should lead a campaign to urge Internet social media companies to include Privacy by Design principles in their user agreements. This social media privacy campaign would follow librarians’ historical privacy advocacy efforts, and promoting ethical user agreements presents a new venue for librarians’ advocacy in the era of online information access

Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models

Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case study using a version of the Enron e-mail corpus dataset demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy \emph{and} supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analyzing such data. We use a simple yet effective randomized response mechanism to generate synthetic networks under $\epsilon$-edge differential privacy, and then use likelihood based inference for missing data and Markov chain Monte Carlo techniques to fit exponential-family random graph models to the generated synthetic networks.Comment: Updated, 39 page

A privacy-preserving model to control social interaction behaviors in social network sites

Social Network Sites (SNSs) served as an invaluable platform to transfer information across a large number of users. SNSs also disseminate users data to third-parties to provide more interesting services for users as well as gaining profits. Users grant access to third-parties to use their services, although they do not necessarily protect users’ data privacy. Controlling social network data diffusion among users and third-parties is difficult due to the vast amount of data. Hence, undesirable users’ data diffusion to unauthorized parties in SNSs may endanger users’ privacy. This paper highlights the privacy breaches on SNSs and emphasizes the most significant privacy issues to users. The goals of this paper are to i) propose a privacy-preserving model for social interactions among users and third-parties; ii) enhance users’ privacy by providing access to the data for appropriate third-parties. These advocate to not compromising the advantages of SNSs information sharing functionalities

Facebook and the Commercialisation of Personal Information: Some Questions of Provider-to-User Privacy

Most of the debate about online social networking sites, such as Facebook, has thus far revolved around questions of privacy and access to personal information. Users of such services, should they choose to exercise them, have a myriad of privacy options that allow them to restrict access to their own personal information posted online, and the privacy policies of such sites are abundantly clear that the making of such choices is the responsibility of the Users themselves. However, due to the focus resting upon these peer-to-peer privacy questions, those relating to the service provider-to-User relationship have been overlooked. This paper seeks to highlight some of the more subtle privacy issues of (what we will call) the ‘Facebook debate’ in terms of two main considerations: the access to and the control of personal information posted online

Implementation of Privacy Policy Specification System for User Uploaded Images over Popular Content Sharing Sites

The regular use of social networking websites and application encompasses the collection and retention of personal and very often sensitive information about users. This information needs to remain private and each social network owns a privacy policy that describes in-depth how user’s information is managed and published. As there is increasing use of images for sharing through social sites, maintaining privacy has become a major problem. In light of these incidents, the need of tools to aid users control access to their shared content is necessary. This problem can be proposed by using an Privacy Policy Specification system to help users compose privacy settings for their shared images. Toward addressing this need, we propose Privacy Policy Specification system to help users to specify privacy settings for their images. Privacy Policy Specification System configure a policy for a group and apply appropriate policies (comment, share, expiry, download) on image for sharing in the group

Metrics for privacy assessment when sharing information in online social networks

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts¿ scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures.This work was supported in part by the Spanish Government project under Grant TIN2017-89156-R, and in part by the FPI under Grant BES-2015-074498.Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Metrics for privacy assessment when sharing information in online social networks. IEEE Access. 7:143631-143645. https://doi.org/10.1109/ACCESS.2019.2944723S143631143645