Related papers: Reclaiming Privacy and Performance over Centralize…
Differential privacy has emerged as the main definition for private data analysis and machine learning. The {\em global} model of differential privacy, which assumes that users trust the data collector, provides strong privacy guarantees…
Over the last few years, the evolution of network and user handsets' technologies, have challenged the telecom industry and the Internet ecosystem. Especially, the unprecedented progress of multimedia streaming services like YouTube, Vimeo…
Information-Centric Networks (ICN) are promising alternatives to current Internet architecture since the Internet struggles with a number of issues such as scalability, mobility and security. ICN offers a number of potential benefits…
Privacy-preserving distributed processing has received considerable attention recently. The main purpose of these algorithms is to solve certain signal processing tasks over a network in a decentralised fashion without revealing…
Large genomic datasets are now created through numerous activities, including recreational genealogical investigations, biomedical research, and clinical care. At the same time, genomic data has become valuable for reuse beyond their…
Enormous amounts of data collected from social networks or other online platforms are being published for the sake of statistics, marketing, and research, among other objectives. The consequent privacy and data security concerns have…
User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors since their rating data might be exposed…
Privacy issues and communication cost are both major concerns in distributed optimization. There is often a trade-off between them because the encryption methods required for privacy-preservation often incur expensive communication…
Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including…
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns. To address these issues, secure Two-party computation (2PC) has been proposed as a means of enabling privacy-preserving DL computation.…
We perform a passive measurement study investigating how a Protective DNS service might perform in a Research & Education Network serving hundreds of member institutions. Utilizing freely-available DNS blocklists consisting of domain names…
We propose a privacy-preserving ensemble infused enhanced Deep Neural Network (DNN) based learning framework in this paper for Internet-of-Things (IoT), edge, and cloud convergence in the context of healthcare. In the convergence, edge…
The proliferation of connected devices through Internet connectivity presents both opportunities for smart applications and risks to security and privacy. It is vital to proactively address these concerns to fully leverage the potential of…
An Open Network Handle System (ONHS) provides an intermediate level of service between IP numbers and domain names. A handle adheres permanently to an owner, who may assign and reassign it to different addresses at will. But a handle is a…
A large amount of data and applications need to be shared with various parties and stakeholders in the cloud environment for storage, computation, and data utilization. Since a third party operates the cloud platform, owners cannot fully…
To protect user privacy in data analysis, a state-of-the-art strategy is differential privacy in which scientific noise is injected into the real analysis output. The noise masks individual's sensitive information contained in the dataset.…
Deep Neural Network (DNN) Inference in Edge Computing, often called Edge Intelligence, requires solutions to insure that sensitive data confidentiality and intellectual property are not revealed in the process. Privacy-preserving Edge…
We study the task of $(\epsilon, \delta)$-differentially private online convex optimization (OCO). In the online setting, the release of each distinct decision or iterate carries with it the potential for privacy loss. This problem has a…
Most of the existing models for deploying IoT ecosystem involves the vendor being in the loop of the command and control of IoT devices hence users' privacy and security is one of the main challenges. Despite these concerns, users are often…
Taking into account of both the huge computing power of intruders and untrusted cloud servers, we develop an enhanced secure pseudonym scheme to protect the privacy of mobile cloud data. To face the huge computing power challenge, we…