Related papers: Inference Attacks on Encrypted Online Voting via T…

Current electronic voting systems require an anonymous channel during the voting phase to prevent coercion. Typically, low-latency anonymization-networks like Tor are used for this purpose. In this paper we devise a monitoring attack that…

Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several website fingerprinting attacks based on machine learning and…

In traditional access control policies, every access granted and administrative account introduces an additional vulnerability, as a corruption of a high-privilege user can compromise several sensitive files. Privocracy is an access control…

We describe the motivation and design for esINSIDER, an automated tool that detects potential persistent and insider threats in a network. esINSIDER aggregates clues from log data, over extended time periods, and proposes a small number of…

The proliferation of smart home Internet of Things (IoT) devices presents unprecedented challenges for preserving privacy within the home. In this paper, we demonstrate that a passive network observer (e.g., an Internet service provider)…

The Tor network is a widely used system for anonymous communication. However, Tor is known to be vulnerable to attackers who can observe traffic at both ends of the communication path. In this paper, we show that prior attacks are just the…

E-voting systems have emerged as a powerful technology for improving democracy by reducing election cost, increasing voter participation, and even allowing voters to directly verify the entire election procedure. Prior internet voting…

The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to…

Ensuring security and integrity of elections constitutes an important challenge with wide-ranging societal implications. Classically, security guarantees can be ensured based on computational complexity, which may be challenged by quantum…

We present an online voting architecture based on partitioning the election in small clusters of voters and using a new Multi-party Computation algorithm for obtaining voting results from the clusters. This new algorithm has some practical…

This paper reports the results of a survey of 1,976 individuals regarding their opinions on TLS inspection, a controversial technique that can be used for both benevolent and malicious purposes. Responses indicate that participants hold…

Internet voting will probably be one of the most significant achievements of the future information society. It will have an enormous impact on the election process making it fast, reliable and inexpensive. Nonetheless, so far remote voting…

Machine learning algorithms, when applied to sensitive data, pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak specific private information in the training data to…

Data encryption is the primary method of protecting the privacy of consumer device Internet communications from network observers. The ability to automatically detect unencrypted data in network traffic is therefore an essential tool for…

We present a system for running auditable and verifiable elections in untrusted environments. Votes are anonymous since the order of candidates on a ballot sheet is random. Tellers see only the position of the candidate. Voters can check…

NetFlow data is a popular network log format used by many network analysts and researchers. The advantages of using NetFlow over deep packet inspection are that it is easier to collect and process, and it is less privacy intrusive. Many…

With the ever-increasing reliance on data for data-driven applications in power grids, such as event cause analysis, the authenticity of data streams has become crucially important. The data can be prone to adversarial stealthy attacks…

Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties. It is renowned for preserving privacy as the data never leaves the computational devices, and recent approaches…

As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely…

Graph embeddings have been proposed to map graph data to low dimensional space for downstream processing (e.g., node classification or link prediction). With the increasing collection of personal data, graph embeddings can be trained on…