Related papers: Towards Plugging Privacy Leaks in Domain Name Syst…
Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data…
This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks. First, we propose to quantitatively measure the trade-off between model accuracy and privacy losses…
The Domain Name System (DNS) service is one of the pillars of the Internet. This service allows users to access websites on the Internet through easy-to-remember domain names rather than complex numeric IP addresses. DNS acts as a directory…
Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data…
The ever-growing advances of deep learning in many areas including vision, recommendation systems, natural language processing, etc., have led to the adoption of Deep Neural Networks (DNNs) in production systems. The availability of large…
In this paper, the problem of providing privacy to users requesting data over a network from a distributed storage system (DSS) is considered. The DSS, which is considered as the multi-terminal destination of the network from the user's…
The current Domain Name System (DNS) infrastructure faces critical vulnerabilities including poisoning attacks, censorship mechanisms, and centralized points of failure that compromise internet freedom and security. Recent incidents such as…
Centralized DNS over HTTPS/TLS (DoH/DoT) resolution, which has started being deployed by major hosting providers and web browsers, has sparked controversy among Internet activists and privacy advocates due to several privacy concerns. This…
There is a continuous increase in the sophistication that modern malware exercise in order to bypass the deployed security mechanisms. A typical approach to evade the identification and potential takedown of a botnet command and control…
Domain Name System (DNS) domains became Internet-level identifiers for entities (like companies, organizations, or individuals) hosting services and sharing resources over the Internet. Domains can specify a set of security policies (such…
Unencrypted DNS traffic between users and DNS resolvers can lead to privacy and security concerns. In response to these privacy risks, many browser vendors have deployed DNS-over-HTTPS (DoH) to encrypt queries between users and DNS…
Network embedding represents network nodes by a low-dimensional informative vector. While it is generally effective for various downstream tasks, it may leak some private information of networks, such as hidden private links. In this work,…
Despite increasing advancements in today's information exchange infrastructure, the preservation of user data and privacy still remains a problem. Both insecure baselines and secure solutions leak user data. For example, Certificate…
With the emergence of smart cities, Internet of Things (IoT) devices as well as deep learning technologies have witnessed an increasing adoption. To support the requirements of such paradigm in terms of memory and computation, joint and…
The DNS infrastructure is infamous for facilitating reflective amplification attacks. Various countermeasures such as server shielding, access control, rate limiting, and protocol restrictions have been implemented. Still, the threat…
Over the last decade, Web traffic has significantly shifted towards HTTPS due to an increased awareness for privacy. However, DNS traffic is still largely unencrypted, which allows user profiles to be derived from plaintext DNS queries.…
The Domain Name System (DNS) provides a translation between readable domain names and IP addresses. The DNS is a key infrastructure component of the Internet and a prime target for a variety of attacks. One of the most significant threat to…
Recent developments in deep learning have led to great success in various natural language processing (NLP) tasks. However, these applications may involve data that contain sensitive information. Therefore, how to achieve good performance…
End-users are concerned about protecting the privacy of their sensitive personal data that are generated while working on information systems. This extends to both the data they actively provide including personal identification in exchange…
Most online communications rely on DNS to map domain names to their hosting IP address(es). Previous work has shown that DNS-based network interference is widespread due to the unencrypted and unauthenticated nature of the original DNS…