Parking Tickets for Privacy-Preserving Pay-by-Phone Parking

This document is a postprint version of the paper presented at the 18th Workshop on Privacy in the Electronic Society (WPES’19), November 11, 2019, London (United Kingdom).Traditionally, the payment required for parking in regulated areas has been made through parking meters. In the last years, several applications which allow to perform these payments using a mobile device have appeared. In this paper we propose a privacy-preserving pay-by-phone parking system o ering the same privacy as the traditional paper- based method even assuming an internal attacker with full access to all the information managed by the system servers. Drivers'privacy is preserved without requiring them to trust any party. Furthermore, the system can tolerate that the mobile devices of drivers fall out of coverage while their cars are parked

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE

Efficient, Effective, and Realistic Website Fingerprinting Mitigation

Website fingerprinting attacks have been shown to be able to predict the website visited even if the network connection is encrypted and anonymized. These attacks have achieved accuracies as high as 92%. Mitigations to these attacks are using cover/decoy network traffic to add noise, padding to ensure all the network packets are the same size, and introducing network delays to confuse an adversary. Although these mitigations have been shown to be effective, reducing the accuracy to 10%, the overhead is high. The latency overhead is above 100% and the bandwidth overhead is at least 30%. We introduce a new realistic cover traffic algorithm, based on a user’s previous network traffic, to mitigate website fingerprinting attacks. In simulations, our algorithm reduces the accuracy of attacks to 14% with zero latency overhead and about 20% bandwidth overhead. In real-world experiments, our algorithms reduces the accuracy of attacks to 16% with only 20% bandwidth overhead

Documenting Privacy Dark Patterns: How Social Networking Sites Influence Users’ Privacy Choices

Dark patterns are user interface (UI) design strategies intended to influence users to make choices or perform actions that benefit online services. This study examines the dark patterns employed by social networking sites (SNSs) to influence users to make privacy-invasive choices. We documented the privacy dark patterns encountered in attempts to register an account, configure account settings, and log in and out for five SNSs popular among American teenagers (Discord, Twitter, Instagram, TikTok, and Snapchat). Based on our observations, we present a typology consisting of three major types of privacy dark patterns (Obstruction, Obfuscation, and Pressure) and 10 subtypes. These strategies undermine the ability of users to make conscious, informed decisions about the management of their personal data – and as prolific users of social media who sometimes demonstrate a lack of knowledge and concern about online privacy, teens are especially vulnerable. We outline the implications of our findings for teens’ privacy on social media and the development of dark pattern countermeasures

Privacy Analysis of Online and Offline Systems

How to protect people's privacy when our life are banded together with smart devices online and offline? For offline systems like smartphones, we often have a passcode to prevent others accessing to our personal data. Shoulder-surfing attacks to predict the passcode by humans are shown to not be accurate. We thus propose an automated algorithm to accurately predict the passcode entered by a victim on her smartphone by recording the video. Our proposed algorithm is able to predict over 92% of numbers entered in fewer than 75 seconds with training performed once.For online systems like surfing on Internet, anonymous communications networks like Tor can help encrypting the traffic data to reduce the possibility of losing our privacy. Each Tor client telescopically builds a circuit by choosing three Tor relays and then uses that circuit to connect to a server. The Tor relay selection algorithm makes sure that no two relays with the same /16 IP address or Autonomous System (AS) are chosen. Our objective is to determine the popularity of Tor relays when building circuits. With over 44 vantage points and over 145,000 circuits built, we found that some Tor relays are chosen more often than others. Although a completely balanced selection algorithm is not possible, analysis of our dataset shows that some Tor relays are over 3 times more likely to be chosen than others. An adversary could potentially eavesdrop or correlate more Tor traffic.Further more, the effectiveness of website fingerprinting (WF) has been shown to have an accuracy of over 90% when using Tor as the anonymity network. The common assumption in previous work is that a victim is visiting one website at a time and has access to the complete network trace of that website. Our main concern about website fingerprinting is its practicality. Victims could visit another website in the middle of visiting one website (overlapping visits). Or an adversary may only get an incomplete network traffic trace. When two website visits are overlapping, the website fingerprinting accuracy falls dramatically. Using our proposed "sectioning" algorithm, the accuracy for predicting the website in overlapping visits improves from 22.80% to 70%. When part of the network trace is missing (either the beginning or the end), the accuracy when using our sectioning algorithm increases from 20% to over 60%

Automating user privacy policy recommendations in social media

Most Social Media Platforms (SMPs) implement privacy policies that enable users to protect their sensitive information against privacy violations. However, observations indicate that users find these privacy policies cumbersome and difficult to configure. Consequently, various approaches have been proposed to assist users with privacy policy configuration. These approaches are however, limited to either protecting only profile attributes, or only protecting user-generated content. This is problematic, because both profile attributes and user-generated content can contain sensitive information. Therefore, protecting one without the other, can still result in privacy violations. A further drawback of existing approaches is that most require considerable user input which is time consuming and inefficient in terms of privacy policy configuration. In order to address these problems, we propose an automated privacy policy recommender system. The system relies on the expertise of existing social media users, as well as the user's privacy policy history in order to provide him/her with personalized privacy policy suggestions for both profile attributes, and user-generated content. Results from our prototype implementation indicate that the proposed recommender system provides accurate privacy policy suggestions, with minimum user input