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
LocBorg: Hiding Social Media User Location while Maintaining Online Persona.
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
- Award ID(s):
- 1649469
- PAR ID:
- 10074878
- Date Published:
- Journal Name:
- Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2017
- 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
-
Social media embed rich but noisy signals of physical locations of their users. Accurately inferring a user's location can significantly improve the user's experience on the social media and enable the development of new location-based applications. This paper proposes a novel community-based approach for predicting the location of a user by using communities in the egonet of the user. We further propose both geographical proximity and structural proximity metrics to profile communities in the ego-net of a user, and then evaluate the effectiveness of each individual metric on real social media data. We discover that geographical proximity metrics, such as average/median haversine distance and community closeness, are strong indicators of a good community for geotagging. In addition, structural proximity metric conductance performs comparable to geographical proximity metrics while triangle participation ratio and internal density are weak location indicators. To the best of our knowledge, this is the first effort to infer the physical location of a user from the perspective of latent communities in the user's ego-net.more » « less
-
In Location-Based Services (LBS), users are required to disclose their precise location information to query a service provider. An untrusted service provider can abuse those queries to infer sensitive information on a user through spatio-temporal and historical data analyses. Depicting the drawbacks of existing privacy-preserving approaches in LBS, we propose a user-centric obfuscation approach, called KLAP, based on the three fundamental obfuscation requirements: k number of locations, l-diversity, and privacy area preservation. Considering user's sensitivity to different locations and utilizing Real-Time Traffic Information (RTTI), KLAP generates a convex Concealing Region (CR) to hide user's location such that the locations, forming the CR, resemble similar sensitivity and are resilient against a wide range of inferences in spatio-temporal domain. For the first time, a novel CR pruning technique is proposed to significantly improve the delay between successive CR submissions. We carry out an experiment with a real dataset to show its effectiveness for sporadic, frequent, and continuous service use cases.more » « less
-
People increasingly share personal information, including their photos and photo collections, on social media. This information, however, can compromise individual privacy, particularly as social media platforms use it to infer detailed models of user behavior, including tracking their location. We consider the specific issue of location privacy as potentially revealed by posting photo collections, which facilitate accurate geolocation with the help of deep learning methods even in the absence of geotags. One means to limit associated inadvertent geolocation privacy disclosure is by carefully pruning select photos from photo collections before these are posted publicly. We study this problem formally as a combinatorial optimization problem in the context of geolocation prediction facilitated by deep learning. We first demonstrate the complexity both by showing that a natural greedy algorithm can be arbitrarily bad and by proving that the problem is NP-Hard. We then exhibit an important tractable special case, as well as a more general approach based on mixed-integer linear programming. Through extensive experiments on real photo collections, we demonstrate that our approaches are indeed highly effective at preserving geolocation privacy.more » « less
An official website of the United States government

