Over the past decade, researchers have started to explore the use of NLP to develop tools aimed at helping the public, vendors, and regulators analyze disclosures made in privacy policies. With the introduction of new privacy regulations, the language of privacy policies is also evolving, and disclosures made by the same organization are not always the same in different languages, especially when used to communicate with users who fall under different jurisdictions. This work explores the use of language technologies to capture and analyze these differences at scale. We introduce an annotation scheme designed to capture the nuances of two new landmark privacy regulations, namely the EU’s GDPR and California’s CCPA/CPRA. We then introduce the first bilingual corpus of mobile app privacy policies consisting of 64 privacy policies in English (292K words) and 91 privacy policies in German (478K words), respectively with manual annotations for 8K and 19K fine-grained data practices. The annotations are used to develop computational methods that can automatically extract “disclosures” from privacy policies. Analysis of a subset of 59 “semi-parallel” policies reveals differences that can be attributed to different regulatory regimes, suggesting that systematic analysis of policies using automated language technologies is indeed a worthwhile endeavor.

Abstract Browsing privacy tools can help people protect their digital privacy. However, tools which provide the strongest protections—such as Tor Browser—have struggled to achieve widespread adoption. This may be due to usability challenges, misconceptions, behavioral biases, or mere lack of awareness. In this study, we test the effectiveness of nudging interventions that encourage the adoption of Tor Browser. First, we test an informational nudge based on protection motivation theory (PMT), designed to raise awareness of Tor Browser and help participants form accurate perceptions of it. Next, we add an action planning implementation intention, designed to help participants identify opportunities for using Tor Browser. Finally, we add a coping planning implementation intention, designed to help participants overcome challenges to using Tor Browser, such as extreme website slowness. We test these nudges in a longitudinal field experiment with 537 participants. We find that our PMT-based intervention increased use of Tor Browser in both the short- and long-term. Our coping planning nudge also increased use of Tor Browser, but only in the week following our intervention. We did not find statistically significant evidence of our action planning nudge increasing use of Tor Browser. Our study contributes to a greater understanding of factors influencing the adoption of Tor Browser, and how nudges might be used to encourage the adoption of Tor Browser and similar privacy enhancing technologies.

Strangely enough, it is possible to use machine learning models to predict the satisfiability status of hard SAT problems with accuracy considerably higher than random guessing. Existing methods have relied on extensive, manual feature engineering and computationally complex features (e.g., based on linear programming relaxations). We show for the first time that even better performance can be achieved by end-to-end learning methods — i.e., models that map directly from raw problem inputs to predictions and take only linear time to evaluate. Our work leverages deep network models which capture a key invariance exhibited by SAT problems: satisfiability status is unaffected by reordering variables and clauses. We showed that end-to-end learning with deep networks can outperform previous work on random 3-SAT problems at the solubility phase transition, where: (1) exactly 50% of problems are satisfiable; and (2) empirical runtimes of known solution methods scale exponentially with problem size (e.g., we achieved 84% prediction accuracy on 600-variable problems, which take hours to solve with state-of-the-art methods). We also showed that deep networks can generalize across problem sizes (e.g., a network trained only on 100-variable problems, which typically take about 10 ms to solve, achieved 81% accuracy on 600-variable problems).

A primary goal of intelligent transportation systems (ITS) is to improve road safety. The ability for vehicles to communicate is a promising way to alleviate traffic accidents by reducing the response time associated with human reaction to nearby drivers. In addition the limitations of standard driving can be overcome by providing drivers with instantaneous information about complications up ahead. Shockwaves, induced by vehicle speed differentials, are a typical mobility pattern that occurs with the formation and propagation of vehicle queues. These induce sudden braking and increase the occurrence of traffic incidents. In this paper, we investigate safety applications in highways with shockwave mobility and different lane configurations in vehicular ad hoc networks (VANET). We evaluate the performance of multi-hop broadcast communication using the ns-2 simulator with vehicles following a shockwave mobility pattern in fully-connected traffic streams. We propose mechanism to improve broadcast reliability using dynamic transmission range that leverages our understanding of fundamental traffic flow relationships.

Inter-vehicle communication is a promising way to share and disseminate real-time and nearby safety information on the road. However, several pressing open questions require solutions in order to achieve high reliability and efficiency with these systems. Further, previous studies show that mobility model can significantly influence the communication performance in vehicular networks. In this paper, we analyze communication in stop-and-go waves and propose a method to optimize an important network parameter, the transmission range, based on traffic pattern measures. Our findings suggest a transmission range adjustment scheme that achieves high reliability by considering network coverage and packet reception rates.

Routing in wireless ad hoc networks are vulnerable to traffic analysis, spoofing and denial of service attacks due to open wireless medium communications. Anonymity mechanisms in ad hoc networks are critical security measures used to mitigate these problems by concealing identification information, such as those of nodes, links, traffic flows, paths and network topology information from harmful attackers. We propose ODAR, an On-Demand Anonymous Routing protocol for wireless ad hoc networks to enable complete anonymity of nodes, links and source-routing paths/trees using Bloom filters. We simulate ODAR using J-Sim, and compare its performance with AODV in certain ad hoc network scenarios