29,659 research outputs found
PrivCheck: Privacy-Preserving Check-in Data Publishing for Personalized Location Based Services
International audienceWith the widespread adoption of smartphones, we have observed an increasing popularity of Location-Based Services (LBSs) in the past decade. To improve user experience, LBSs often provide personalized recommendations to users by mining their activity (i.e., check-in) data from location-based social networks. However, releasing user check-in data makes users vulnerable to inference attacks, as private data (e.g., gender) can often be inferred from the users'check-in data. In this paper, we propose PrivCheck, a customizable and continuous privacy-preserving check-in data publishing framework providing users with continuous privacy protection against inference attacks. The key idea of PrivCheck is to obfuscate user check-in data such that the privacy leakage of user-specified private data is minimized under a given data distortion budget, which ensures the utility of the obfuscated data to empower personalized LBSs. Since users often give LBS providers access to both their historical check-in data and future check-in streams, we develop two data obfuscation methods for historical and online check-in publishing, respectively. An empirical evaluation on two real-world datasets shows that our framework can efficiently provide effective and continuous protection of user-specified private data, while still preserving the utility of the obfuscated data for personalized LBS
Maintain Data Integrity And Protection Of Private Label Information In Social Network Data
Perceptive information about users of the social networks should be protected. The confront is to plan methods to publish social network data in a form that affords usefulness without compromising privacy. Previous research has proposed a variety of privacy models with the corresponding protection mechanisms that put off both unintentional private information escape and attacks by malicious adversaries. These early privacy models are mainly disturbed with identity and link revelation. The social networks are modelled as graphs in which users are nodes and social connections are edges. The intimidation definitions and defence mechanisms leverage structural properties of the graph. This paper is stimulated by the recognition of the need for a better grain and more personalized privacy. Users commend social networks such as Face book and LinkedIn with a wealth of personal information such as their age, address, current location or political orientation. We refer to these details and messages as features in the user's profile. We propose a privacy protection scheme that not only put off the revelation of identity of users but also the disclosure of selected features in users' profiles
Privacy Intelligence: A Survey on Image Sharing on Online Social Networks
Image sharing on online social networks (OSNs) has become an indispensable
part of daily social activities, but it has also led to an increased risk of
privacy invasion. The recent image leaks from popular OSN services and the
abuse of personal photos using advanced algorithms (e.g. DeepFake) have
prompted the public to rethink individual privacy needs when sharing images on
OSNs. However, OSN image sharing itself is relatively complicated, and systems
currently in place to manage privacy in practice are labor-intensive yet fail
to provide personalized, accurate and flexible privacy protection. As a result,
an more intelligent environment for privacy-friendly OSN image sharing is in
demand. To fill the gap, we contribute a systematic survey of 'privacy
intelligence' solutions that target modern privacy issues related to OSN image
sharing. Specifically, we present a high-level analysis framework based on the
entire lifecycle of OSN image sharing to address the various privacy issues and
solutions facing this interdisciplinary field. The framework is divided into
three main stages: local management, online management and social experience.
At each stage, we identify typical sharing-related user behaviors, the privacy
issues generated by those behaviors, and review representative intelligent
solutions. The resulting analysis describes an intelligent privacy-enhancing
chain for closed-loop privacy management. We also discuss the challenges and
future directions existing at each stage, as well as in publicly available
datasets.Comment: 32 pages, 9 figures. Under revie
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Online advertising: analysis of privacy threats and protection approaches
Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft
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