Online privacy policies are lengthy and hard to read, yet are profoundly important as they communicate the practices of an organization pertaining to user data privacy. Privacy Enhancing Technologies, or PETs, seek to inform users by summarizing these privacy policies. Efforts in the research and development of such PETs, however, have largely been limited to tools that recap the policy or visualize it. We present the next generation of our research and publicly available tool, PrivacyCheck v3, that utilizes machine learning to inform and empower users with respect to privacy policies. PrivacyCheck v3 adds capabilities that are commonly absent from similar PETs on the web. In particular, it adds the ability to (1) find the competitors of an organization with Alexa traffic analysis and compare policies across them, (2) follow privacy policies to which the user has agreed and notify the user when policies change, (3) track policies over time and report how often policies change and their trends, (4) automatically find privacy policies in domains, and (5) provide a bird's-eye view of privacy policies. The new features of PrivacyCheck not only inform users about details of privacy policies, but also empower them to understand privacy policies at a higher level, make informed decisions, and even select competitors with better privacy policies.

PrivacyCheck v3: Empowering Users with Higher-Level Understanding of Privacy Policies

Evaluating the Effect of Justification and Confidence Information on User Perception of a Privacy Policy Summarization Tool.

Since GDPR was introduced, there is a reinforcement of the fact that users must give their consent before their personal data can be managed by any website. However, many studies have demonstrated that users often skip these policies and click the "I agree" button to continue browsing, being unaware of what the consent they gave was about, hence defeating the purpose of GDPR. This paper investigates if different ways of presenting users the privacy policy can change this behaviour and can lead to an increased awareness of the user in relation to what the user agrees with. Three different types of policies were used in the study: a full-text policy, a so-called usable policy, and a video-based policy. Results demonstrated that the type of policy has a direct influence on the user awareness and user satisfaction. The two alternatives to the text-based policy lead to a significant increase of user awareness in relation to the content of the policy and to a significant increase in the user satisfaction in relation to the usability of the policy.

Privacy Policy – “I agree”?! – Do alternatives to text-based policies increase the awareness of the users?

Detecting One-Pixel Attacks Using Variational Autoencoders

The complexity of privacy policies makes it difficult for users to understand its content. In order to solve this, tools exist that analyze and summarize those privacy policies, and present the results in a standardized visual format. The use of these tools can make it possible to analyze any privacy policy, that is, they have the advantage of scale, unlike processes that require manual classification. However, there is scarce research on their effectiveness and how users perceive them. In this paper, an experimental survey was conducted to evaluate whether one such tool, PrivacyGuide, could communicate risk and increase interest in the content of the privacy policy itself. The survey was conducted in Japan with Japanese participants, and considered two languages of the privacy policy, Japanese and English. The results show that interest in the privacy policy increased after viewing the privacy policy summary. On the other hand, risk communication was limited to the case of an English language privacy policy. In addition, survey participants also provided positive and negative feedback about the tool: there was interest in using the tool in a variety of scenarios, but there was also lack of trust in the results. The findings suggest that privacy policy summarization tools have potential to help users, but that there are barriers for adoption of the tool.

User Study of the Effectiveness of a Privacy Policy Summarization Tool

Summarization and visualization applications can help users understand the content of privacy policies. However, research has focused on English language privacy policies and has not considered users who are not native English speakers nor the potential situation of encountering a privacy policy in a foreign language. In this paper, we contribute to the research on privacy policy summarization by conducting an experimental survey on Japanese users to assess their interest on using such an application, and the influence of this application on their perception. We conducted an experimental survey among Japanese participants, and evaluated their perception on different privacy policy languages (Japanese or English) and risk levels, using PrivacyGuide. We found that PrivacyGuide can increase interest in the contents of the privacy policy for both languages, and can communicate risk level for the English privacy policy. In addition, we found that respondents who indicated interest in using the application mentioned a wide variety of scenarios for its use, while respondents who answered negatively or were hesitant mentioned lack of trust and uncertainty about PrivacyGuide’s reputation and accuracy. We discuss these results and offer suggestions for improving adoption of privacy policy summarization tools like PrivacyGuide.

Evaluating Privacy Policy Summarization: An Experimental Study among Japanese Users.

IoT Data Privacy

Adversarial images have been proposed to deceive deep neural networks (DNNs) by adding perturbations to the pixels. Unlike existing attacks, Su et al. [1] analyzed an attack in an extremely limited constraint where only one pixel was modified. However, their one-pixel attack is easy to recognize by humans. In this paper, we improve the attack to enable the deceit of both DNNs and humans. We conducted a human recognition analysis to prove our attack's effect. We then propose detection and defense methods against the attack by re-attacking the adversarial images. Our experimental results on the six most recent convolutional neural networks show that while our attack achieved approximately the same success rates and confidence scores as in the existing attack, our attack achieves a higher success rate for deceiving humans. Only 49.41 % of participants can recognize our attack even though 81.04 % participants have recognized the existing attack. OPA2D detects 99.33% of the existing attack and 100% of our attack and defends 92.00% of the existing attack and 95.33 % of our attack.

OPA2D: One-Pixel Attack, Detection, and Defense in Deep Neural Networks

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