Investigating the Privacy vs. Forwarding Accuracy Tradeoff in Opportunistic Interest-Casting

Many mobile social networking applications are based on a ``friend proximity detection" step, according to which two mobile users try to jointly estimate whether they have friends in common, or share similar interests, etc. Performing ``friend proximity detection" in a privacy-preserving way is fundamental to achieve widespread acceptance of mobile social networking applications. However, the need of privacy preservation is often at odds with application-level performance of the mobile social networking application, since only obfuscated information about the other user\u27s profile is available for optimizing performance. noindent In this paper, we study for the first time the fundamental tradeoff between privacy preservation and application-level performance in mobile social networks. More specifically, we consider a mobile social networking application for opportunistic networks called interest-casting. In the interest-casting model, a user wants to deliver a piece of information to other users sharing similar interests (``friends"), possibly through multi-hop forwarding. In this paper, we propose a privacy-preserving friend proximity detection scheme based on a protocol for solving the Yao\u27s ``Millionaire\u27s Problem", and we introduce three interest-casting protocols achieving different tradeoffs between privacy and accuracy of the information forwarding process. The privacy vs. accuracy tradeoff is analyzed both theoretically, and through simulations based on a real-world mobility trace. The results of our study demonstrate for the first time that privacy preservation is at odds with forwarding accuracy, and that the best tradeoff between these two conflicting goals should be identified based on the application-level requirements

Security and Privacy in Heterogeneous Wireless and Mobile Networks: Challenges and Solutions

abstract: The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.Dissertation/ThesisPh.D. Electrical Engineering 201

Recessive Social Networking:Preventing Privacy Leakage against Reverse Image Search

This work investigates the image privacy problem in the context of social networking under the threat of reverse image search. We introduce a new concept called recessive social networking. Unlike conventional privacy-preserving social networking, in our setting, the aim is to deceive machine learning algorithms that used in reverse image search, while still enabling unaffected ubiquitous social networking among humans. We, for the first time, ultilize adversarial example technique as a defensive mechanism to protect image privacy against content-based image search algorithms in the context of social networking. Finally, rigorous evaluations are conducted to demonstrate the effectiveness, transferability, and robustness of the proposed countermeasure

Remorabook: Privacy-preserving Social Networking Based on Remora Computing

Recent scandals on online social networking have greatly raised privacy concerns on massive amount of personal information stored on social networking platforms. The privacy issues are rooted in the current design of online social networking. On one hand, users have to share their personal information with social networking service providers for networking. On the other hand, the sharing essentially allows the service providers to own the data, and the sharing may results in various privacy issues due to the business model of the service providers. In this thesis, we propose RemoraBook to solve the privacy issues in online social networking with Remora Computing, inspired by the remora fishes noted for traveling effortlessly by attaching themselves to large marine animals such as sharks. Remora Computing enables RemoraBook users to utilize facilities available from service providers to build social networks without sharing information to service providers. The networking function and messaging function of RemoraBook are implemented based on Facebook and Gmail facilities respectively. Our extensive experiments on RemoraBook show social networks can be reliably built in RemoraBook without significant degradation on usability

Privacy-preserving friend recommendations in online social networks

Online social networks, such as Facebook and Google+, have been emerging as a new communication service for users to stay in touch and share information with family members and friends over the Internet. Since the users are generating huge amounts of data on social network sites, an interesting question is how to mine this enormous amount of data to retrieve useful information. Along this direction, social network analysis has emerged as an important tool for many business intelligence applications such as identifying potential customers and promoting items based on their interests. In particular, since users are often interested to make new friends, a friend recommendation application provides the medium for users to expand his/her social connections and share information of interest with more friends. Besides this, it also helps to enhance the development of the entire network structure. The existing friend recommendation methods utilize social network structure and/or user profile information. However, these methods can no longer be applicable if the privacy of users is taken into consideration. This work introduces a set of privacy-preserving friend recommendation protocols based on different existing similarity metrics in the literature. Briefly, depending on the underlying similarity metric used, the proposed protocols guarantee the privacy of a user\u27s personal information such as friend lists. These protocols are the first to make the friend recommendation process possible in privacy-enhanced social networking environments. Also, this work considers the case of outsourced social networks, where users\u27 profile data are encrypted and outsourced to third-party cloud providers who provide social networking services to the users. Under such an environment, this work proposes novel protocols for the cloud to do friend recommendations in a privacy-preserving manner --Abstract, page iii

ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions in the Wild

Recording the dynamics of unscripted human interactions in the wild is challenging due to the delicate trade-offs between several factors: participant privacy, ecological validity, data fidelity, and logistical overheads. To address these, following a 'datasets for the community by the community' ethos, we propose the Conference Living Lab (ConfLab): a new concept for multimodal multisensor data collection of in-the-wild free-standing social conversations. For the first instantiation of ConfLab described here, we organized a real-life professional networking event at a major international conference. Involving 48 conference attendees, the dataset captures a diverse mix of status, acquaintance, and networking motivations. Our capture setup improves upon the data fidelity of prior in-the-wild datasets while retaining privacy sensitivity: 8 videos (1920x1080, 60 fps) from a non-invasive overhead view, and custom wearable sensors with onboard recording of body motion (full 9-axis IMU), privacy-preserving low-frequency audio (1250 Hz), and Bluetooth-based proximity. Additionally, we developed custom solutions for distributed hardware synchronization at acquisition, and time-efficient continuous annotation of body keypoints and actions at high sampling rates. Our benchmarks showcase some of the open research tasks related to in-the-wild privacy-preserving social data analysis: keypoints detection from overhead camera views, skeleton-based no-audio speaker detection, and F-formation detection.Comment: v2 is the version submitted to Neurips 2022 Datasets and Benchmarks Trac

Privacy considerations for secure identification in social wireless networks

This thesis focuses on privacy aspects of identification and key exchange schemes for mobile social networks. In particular, we consider identification schemes that combine wide area mobile communication with short range communication such as Bluetooth, WiFi. The goal of the thesis is to identify possible security threats to personal information of users and to define a framework of security and privacy requirements in the context of mobile social networking. The main focus of the work is on security in closed groups and the procedures of secure registration, identification and invitation of users in mobile social networks. The thesis includes an evaluation of the proposed identification and key exchange schemes and a proposal for a series of modifications that augments its privacy-preserving capabilities. The ultimate design provides secure and effective identity management in the context of, and in respect to, the protection of user identity privacy in mobile social networks

Media Coverage of Online Social Network Privacy Issues in Germany: A Thematic Analysis

The massive growth of online social networking platforms in recent years has brought with it increasing privacy concerns. Rarely do long periods of time pass without some major privacy issue being brought to light. In this paper we present our study regarding media coverage of privacy issues in social networks, and the effects thereof. We gathered all news articles from over 30 German media sources, published between September 1st, 2007 and August 31st, 2008. Using ‘thematic analysis’, we categorized and coded those articles, and identified the major themes among them. The results showed the main themes of interest in the media, and, more importantly, the very significant effect media coverage has on providers, and on the redesign of privacy-compromising features. The results emphasized the need for more regulation of privacy-preserving practices. This study also set the stage for further inquiries into the topic in U.S. media

Privacy-Preserving Interest Matching for Mobile Social Networking

The success of online social networking has resulted in increased attention to mobile social networking research and applications. In mobile social networking, instead of looking for friends over the Internet, people look for friends who are physically located close and also based on other self-defined criteria. For example, a person could find other people who are nearby and who also share the same interests with her by using mobile social networking. As a result, they have common topics to talk about and may eventually become friends. There are two main approaches in the existing works. One approach focuses on efficiently establishing friendship and ignores the protection of private information of the participants. For example, some applications simply broadcast users’ personal information to everybody and rely on the other users to report the matches. From a privacy point of view, this approach is bad, since it makes the users vulnerable to context-aware attacks. The other approach requires a central server to participate in each matchmaking process. For example, an application deploys a central server, which stores the profile information of all users. When two nearby client devices query the central server at the same time, the central server fetches the profile information of both devices from the server’s database, performs matching based on the information, and reports the result back to the clients. However, a central server is not always available, so this approach does not scale. In addition, the central server not only learns all users’ personal information, it also learns which users become friends. This thesis proposes a privacy-preserving architecture for users to find potential friends with the same interests. The architecture has two matchmaking protocols to prevent privacy leaks. Our protocols let a user learn only the interests she has in common with the other party. One protocol is simpler, but works only if some assumptions hold. The other protocol is more secure, but requires longer execution time. Our architecture does not require any central server that is involved in the matchmaking process. We describe how the protocols work, analyze how secure the protocols are under different assumptions, and implement the protocols in a BlackBerry application. We test the efficiency of the protocols by conducting a number of experiments. We also consider the cheating-detection and friend-recognition problems