Related papers: DLPFS: The Data Leakage Prevention FileSystem

Open software ecosystems are beneficial for customers; they benefit from 3rd party services and applications, e.g. analysis of data using apps, developed and deployed by other companies or open-source communities. One significant advantage…

Big data is a term used for a very large data sets that have many difficulties in storing and processing the data. Analysis this much amount of data will lead to information loss. The main goal of this paper is to share data in a way that…

In recent years, traditional cybersecurity safeguards have proven ineffective against insider threats. Famous cases of sensitive information leaks caused by insiders, including the WikiLeaks release of diplomatic cables and the Edward…

The proliferation of peer-to-peer (P2P) file sharing protocols is due to their efficient and scalable methods for data dissemination to numerous users. But many of these networks have no provisions to provide users with long term access to…

Privacy-preserving data processing refers to the methods and models that allow computing and analyzing sensitive data with a guarantee of confidentiality. As cloud computing and applications that rely on data continue to expand, there is an…

As tremendous amount of data being generated everyday from human activity and from devices equipped with sensing capabilities, cloud computing emerges as a scalable and cost-effective platform to store and manage the data. While benefits of…

Federated learning (FL) is a distributed learning process where the model (weights and checkpoints) is transferred to the devices that posses data rather than the classical way of transferring and aggregating the data centrally. In this…

In recent years, Local Differential Privacy (LDP), a robust privacy-preserving methodology, has gained widespread adoption in real-world applications. With LDP, users can perturb their data on their devices before sending it out for…

With the advent of cloud computing, thousands of machines are connected and managed collectively. This era is confronted with a new challenge: performance variability, primarily caused by large-scale management issues such as hardware…

One of the data security and privacy concerns is of insider threats, where legitimate users of the system abuse the access privileges they hold. The insider threat to data security means that an insider steals or leaks sensitive personal…

Federated learning (FL) is increasingly deployed among multiple clients to train a shared model over decentralized data. To address privacy concerns, FL systems need to safeguard the clients' data from disclosure during training and control…

Differential privacy is an information theoretic constraint on algorithms and code. It provides quantification of privacy leakage and formal privacy guarantees that are currently considered the gold standard in privacy protections. In this…

We propose Data Tumbling Layer (DTL), a cryptographic scheme for non-interactive data tumbling. The core concept is to enable users to commit to specific data and subsequently re-use to the encrypted version of these data across different…

Local Differential Privacy (LDP) is the gold standard trust model for privacy-preserving machine learning by guaranteeing privacy at the data source. However, its application to image data has long been considered impractical due to the…

Crowdsourcing enables application developers to benefit from large and diverse datasets at a low cost. Specifically, mobile crowdsourcing (MCS) leverages users' devices as sensors to perform geo-located data collection. The collection of…

Leaks from password datasets are a regular occurrence. An organization may defend a leak with reassurances that just a small subset of passwords were taken. In this paper we show that the leak of a relatively small number of text-based…

This paper introduces the notion of a secure data capsule, which refers to an encapsulation of sensitive user information (such as a credit card number) along with code that implements an interface suitable for the use of such information…

Federated learning(FL) is an emerging distributed learning paradigm with default client privacy because clients can keep sensitive data on their devices and only share local training parameter updates with the federated server. However,…

Since it is impossible to predict and identify all the vulnerabilities of a network beforehand, and penetration into a system by malicious intruders cannot always be prevented, intrusion detection systems (IDSs) are essential entities to…

Cloud platforms host thousands of tenants that demand POSIX semantics, high throughput, and rapid evolution from their storage layer. Kernel-native distributed file systems supply raw speed, but their privileged code base couples every…