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The ability to enforce robust and dynamic access controls on cloud-hosted data while simultaneously ensuring confidentiality with respect to the cloud itself is a clear goal for many users and organizations. To this end, there has been much…
Cloud computing is a popular distributed network and utility model based technology. Since in cloud the data is outsourced to third parties, the protection of confidentiality and privacy of user data becomes important. Different methods for…
In modern datasets, where single records can have multiple owners, enforcing user-level differential privacy requires capping each user's total contribution. This "contribution bounding" becomes a significant combinatorial challenge.…
Data grid is a distributed computing architecture that integrates a large number of data and computing resources into a single virtual data management system. It enables the sharing and coordinated use of data from various resources and…
Local differential privacy (LDP) enables the efficient release of aggregate statistics without having to trust the central server (aggregator), as in the central model of differential privacy, and simultaneously protects a client's…
In the distributed environment, authentication and key exchange mechanisms play a major role. In general, for authentication, the client and the server mutually exchange a common cryptographic key. In earlier, passwords were stored on a…
Privacy Security of data in Cloud Storage is one of the main issues. Many Frameworks and Technologies are used to preserve data security in cloud storage. [1] Proposes a framework which includes the design of data organization structure,…
Grid Computing is a type of parallel and distributed systems that is designed to provide reliable access to data and computational resources in wide area networks. These resources are distributed in different geographical locations, however…
Cloud computing is a term coined to a network that offers incredible processing power, a wide array of storage space and unbelievable speed of computation. Social media channels, corporate structures and individual consumers are all…
Internet of Things (IoT) is a rapidly growing industry currently being integrated into both consumer and industrial environments on a wide scale. While the technology is available and deployment has a low barrier of entry in future…
In P2P systems, large volumes of data are declustered naturally across a large number of peers. But it is very difficult to control the initial data distribution because every user has the freedom to share any data with other users. The…
Today, many organizations are moving their computing services towards the Cloud. This makes their computer processing available much more conveniently to users. However, it also brings new security threats and challenges about safety and…
With the advent of cloud computing, a number of cloud providers have arisen to provide Storage-as-a-Service (SaaS) offerings to both regular consumers and business organizations. SaaS (different than Software-as-a-Service in this context)…
Existing processes and methods for incident handling are geared towards infrastructures and operational models that will be increasingly outdated by cloud computing. Research has shown that to adapt incident handling to cloud computing…
Decentralized learning is an efficient emerging paradigm for boosting the computing capability of multiple bounded computing agents. In the big data era, performing inference within the distributed and federated learning (DL and FL)…
An information owner, possessing diverse data sources, might want to offer information services based on these sources to cooperation partners and to this end interact with these partners by receiving and sending messages, which the owner…
In-network caching is an appealing solution to cope with the increasing bandwidth demand of video, audio and data transfer over the Internet. Nonetheless, an increasing share of content delivery services adopt encryption through HTTPS,…
In this paper, we propose new location privacy preserving schemes for database-driven cognitive radio networks that protect secondary users' (SUs) location privacy while allowing them to learn spectrum availability in their vicinity. Our…
We study a setting of collecting and learning from private data distributed across end users. In the shuffled model of differential privacy, the end users partially protect their data locally before sharing it, and their data is also…
The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called…