Social media streams analysis can reveal the characteristics of people who engage with or write about different topics. Recent works show that it is possible to reveal sensitive attributes (e.g., location, gender, ethnicity, political views, etc.) of individuals by analyzing their social media streams. Although, the prediction of a user's sensitive attributes can be used to enhance the user experience in social media, revealing some attributes like the location could represent a threat on individuals. Users can obfuscate their location by posting about random topics linked to different locations. However, posting about random and sometimes contradictory topics that are not aligned with a user's online persona and posts could negatively affect the followers interested in her profile. This paper represents our vision about the future of user privacy on social media. Users can locally deploy a cyborg, an artificial intelligent system that helps people to defend their privacy on social media. We propose LocBorg, a location privacy preserving cyborg that protects users by obfuscating their location while maintaining their online persona. LocBorg analyzes the social media streams and recommends topics to write about that are similar to a user's topics of interest and aligned with the user's online persona but linked to other locations.
more »
« less
Privacy Cyborg: Towards Protecting the Privacy of Social Media Users
Towards the vision of building artificial intelligence systems that can assist with our everyday life, we introduce a proof of concept for a social media privacy "cyborg" which can locally and privately monitor a person's published content and offer advice or warnings when their privacy is at stake. The idea of a cyborg can be more general, as a separate local entity with its own computational resources, that can automatically perform several online tasks on our behalf. For this demonstration, we assume an attacker that can successfully infer user attributes, solely based on what the user has published (topic-based inference). We focus on Social Media privacy and specifically on the issue of exposing sensitive user-attributes, like location, or race, through published content. We built a privacy cyborg that can monitor a user's posted topics and automatically warn them in real time when a sensitive attribute is at risk of being exposed.
more »
« less
- Award ID(s):
- 1649469
- NSF-PAR ID:
- 10033871
- Date Published:
- Journal Name:
- Data Engineering (ICDE), 2017 IEEE 33rd International Conference on
- Page Range / eLocation ID:
- 1395 to 1396
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Trending Topic Detection has been one of the most popular methods to summarize what happens in the real world through the analysis and summarization of social media content. However, as trending topic extraction algorithms become more sophisticated and report additional information like the characteristics of users that participate in a trend, significant and novel privacy issues arise. We introduce a statistical attack to infer sensitive attributes of Online Social Networks users that utilizes such reported community-aware trending topics. Additionally, we provide an algorithmic methodology that alters an existing community-aware trending topic algorithm so that it can preserve the privacy of the involved users while still reporting topics with a satisfactory level of utility.more » « less
-
null (Ed.)Data and information privacy is a major concern of today’s world. More specifically, users’ digital privacy has become one of the most important issues to deal with, as advancements are being made in information sharing technology. An increasing number of users are sharing information through text messages, emails, and social media without proper awareness of privacy threats and their consequences. One approach to prevent the disclosure of private information is to identify them in a conversation and warn the dispatcher before the conveyance happens between the sender and the receiver. Another way of preventing information (sensitive) loss might be to analyze and sanitize a batch of offline documents when the data is already accumulated somewhere. However, automating the process of identifying user-centric privacy disclosure in textual data is challenging. This is because the natural language has an extremely rich form and structure with different levels of ambiguities. Therefore, we inquire after a potential framework that could bring this challenge within reach by precisely recognizing users’ privacy disclosures in a piece of text by taking into account - the authorship and sentiment (tone) of the content alongside the linguistic features and techniques. The proposed framework is considered as the supporting plugin to help text classification systems more accurately identify text that might disclose the author’s personal or private information.more » « less
-
This paper presents the results of an interview study with twelve TikTok users to explore user awareness, perception, and experiences with the app’s algorithm in the context of privacy. The social media entertainment app TikTok collects user data to cater individualized video feeds based on users’ engagement with presented content which is regulated in a complex and overly long privacy policy. Our results demonstrate that participants generally have very little knowledge of the actual privacy regulations which is argued for with the benefit of receiving free entertaining content. However, participants experienced privacy-related downsides when algorithmically catered video content increasingly adapted to their biography, interests, or location and they in turn realized the detail of personal data that TikTok had access to. This illustrates the tradeoff users have to make between allowing TikTok to access their personal data and having favorable video consumption experiences on the app.more » « less
-
Interdependent privacy (IDP) violations among users occur at a massive scale on social media, as users share or re-share potentially sensitive photos and information about other people without permission. Given that IDP represents a collective moral concern, an ethics of care (or “care ethics”) can inform interventions to promote online privacy. Applied to cyber security and privacy, ethics of care theory puts human relationships at the center of moral problems, where caring-about supports conditions of caring-for and, in turn, protects interpersonal relationships. This position paper explores design implications of an ethics of care framework in the context of IDP preservation. First, we argue that care ethics highlights the need for a network of informed stakeholders involved in content moderation strategies that align with public values. Second, an ethics of care framework calls for psychosocial interventions at the user-level aimed toward promoting more responsible IDP decision-making among the general public. In conclusion, ethics of care has potential to provide coherence in understanding the people involved in IDP, the nature of IDP issues, and potential solutions, in turn, motivating new directions in IDP research.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDE.2017.193
