Related papers: Privacy in Data Service Composition
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…
Modern distributed applications in healthcare, supply chain, and the Internet of Things handle a large amount of data in a diverse application setting with multiple stakeholders. Such applications leverage advanced artificial intelligence…
Data markets serve as crucial platforms facilitating data discovery, exchange, sharing, and integration among data users and providers. However, the paramount concern of privacy has predominantly centered on protecting privacy of data…
Modern websites frequently use and embed third-party services to facilitate web development, connect to social media, or for monetization. This often introduces privacy issues as the inclusion of third-party services on a website can allow…
Privacy-preserving distributed processing has recently attracted considerable attention. It aims to design solutions for conducting signal processing tasks over networks in a decentralized fashion without violating privacy. Many algorithms…
Secure cloud storage is an issue of paramount importance that both businesses and end-users should take into consideration before moving their data to, potentially, untrusted clouds. Migrating data to the cloud raises multiple privacy…
The increasing adoption of Cloud storage poses a number of privacy issues. Users wish to preserve full control over their sensitive data and cannot accept that it to be accessible by the remote storage provider. Previous research was made…
In the Internet of Things and smart environments data, collected from distributed sensors, is typically stored and processed by a central middleware. This allows applications to query the data they need for providing further services.…
Privacy-preservation policies are guidelines formulated to protect data providers private data. Previous privacy-preservation methodologies have addressed privacy in which data are permanently stored in repositories and disconnected from…
Privacy-preserving data splitting is a technique that aims to protect data privacy by storing different fragments of data in different locations. In this work we give a new combinatorial formulation to the data splitting problem. We see the…
Personally identifiable information (PII) can find its way into cyberspace through various channels, and many potential sources can leak such information. Data sharing (e.g. cross-agency data sharing) for machine learning and analytics is…
In the digital era, users share their personal data with service providers to obtain some utility, e.g., access to high-quality services. Yet, the induced information flows raise privacy and integrity concerns. Consequently, cautious users…
As a significant business paradigm, many online information platforms have emerged to satisfy society's needs for person-specific data, where a service provider collects raw data from data contributors, and then offers value-added data…
A growing framework of legal and ethical requirements limit scientific and commercial evalua-tion of personal data. Typically, pseudonymization, encryption, or methods of distributed com-puting try to protect individual privacy. However,…
Data splitting preserves privacy by partitioning data into various fragments to be stored remotely and shared. It supports most data operations because data can be stored in clear as opposed to methods that rely on cryptography. However,…
The current business model for existing recommender services is centered around the availability of users' personal data at their side whereas consumers have to trust that the recommender service providers will not use their data in a…
Privacy is a key challenge for continued digitalization of health. The forthcoming European General Data Protection Regulation (GDPR) is transforming this challenge into regulatory directives. User consent provisioning and coordinating…
Distributed health data networks that use information from multiple sources have drawn substantial interest in recent years. However, missing data are prevalent in such networks and present significant analytical challenges. The current…
Items shared through Social Media may affect more than one user's privacy --- e.g., photos that depict multiple users, comments that mention multiple users, events in which multiple users are invited, etc. The lack of multi-party privacy…
In today's highly connected society, we are constantly asked to provide personal information to retailers, voter surveys, medical professionals, and other data collection efforts. The collected data is stored in large data warehouses.…