Information privacy

About: Information privacy is a research topic. Over the lifetime, 25412 publications have been published within this topic receiving 579611 citations. The topic is also known as: data privacy & data protection.

Blockchain: A Panacea for Healthcare Cloud-Based Data Security and Privacy?

Abstract: One particular trend observed in healthcare is the progressive shift of data and services to the cloud, partly due to convenience (e.g. availability of complete patient medical history in real-time) and savings (e.g. economics of healthcare data management). There are, however, limitations to using conventional cryptographic primitives and access control models to address security and privacy concerns in an increasingly cloud-based environment. In this paper, we study the potential to use the Blockchain technology to protect healthcare data hosted within the cloud. We also describe the practical challenges of such a proposition and further research that is required.

Privacy in e-commerce: examining user scenarios and privacy preferences

Abstract: Privacy is a necessary concern in electronic commerce. It is difficult, if not impossible, to complete a transaction without revealing some personal data ‐ a shipping address, billing information, or product preference. Users may be unwilling to provide this necessary information or even to browse online if they believe their privacy is invaded or threatened. Fortunately, there are technologies to help users protect their privacy. P3P (Platform for Privacy Preferences Project) from the World Wide Web Consortium is one such technology. However, there is a need to know more about the range of user concerns and preferences about privacy in order to build usable and effective interface mechanisms for P3P and other privacy technologies. Accordingly, we conducted a survey of 381 U.S. Net users, detailing a range of commerce scenarios and examining the participants' concerns and preferences about privacy. This paper presents both the findings from that study as well as their design implications.

ProvChain: A Blockchain-based Data Provenance Architecture in Cloud Environment with Enhanced Privacy and Availability

Abstract: Cloud data provenance is metadata that records the history of the creation and operations performed on a cloud data object. Secure data provenance is crucial for data accountability, forensics and privacy. In this paper, we propose a decentralized and trusted cloud data provenance architecture using blockchain technology. Blockchain-based data provenance can provide tamper-proof records, enable the transparency of data accountability in the cloud, and help to enhance the privacy and availability of the provenance data. We make use of the cloud storage scenario and choose the cloud file as a data unit to detect user operations for collecting provenance data. We design and implement ProvChain, an architecture to collect and verify cloud data provenance, by embedding the provenance data into blockchain transactions. ProvChain operates mainly in three phases: (1) provenance data collection, (2) provenance data storage, and (3) provenance data validation. Results from performance evaluation demonstrate that ProvChain provides security features including tamper-proof provenance, user privacy and reliability with low overhead for the cloud storage applications.

Data security and privacy in wireless body area networks

Abstract: The wireless body area network has emerged as a new technology for e-healthcare that allows the data of a patient's vital body parameters and movements to be collected by small wearable or implantable sensors and communicated using short-range wireless communication techniques. WBAN has shown great potential in improving healthcare quality, and thus has found a wide range of applications from ubiquitous health monitoring and computer assisted rehabilitation to emergency medical response systems. The security and privacy protection of the data collected from a WBAN, either while stored inside the WBAN or during their transmission outside of the WBAN, is a major unsolved concern, with challenges coming from stringent resource constraints of WBAN devices, and the high demand for both security/privacy and practicality/usability. In this article we look into two important data security issues: secure and dependable distributed data storage, and fine-grained distributed data access control for sensitive and private patient medical data. We discuss various practical issues that need to be taken into account while fulfilling the security and privacy requirements. Relevant solutions in sensor networks and WBANs are surveyed, and their applicability is analyzed.

Federated Learning via Over-the-Air Computation

Abstract: The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing inapplicable in these scenarios. Instead, edge machine learning becomes increasingly attractive for performing training and inference directly at network edges without sending data to a centralized data center. This stimulates a nascent field termed as federated learning for training a machine learning model on computation, storage, energy and bandwidth limited mobile devices in a distributed manner. To preserve data privacy and address the issues of unbalanced and non-IID data points across different devices, the federated averaging algorithm has been proposed for global model aggregation by computing the weighted average of locally updated model at each selected device. However, the limited communication bandwidth becomes the main bottleneck for aggregating the locally computed updates. We thus propose a novel over-the-air computation based approach for fast global model aggregation via exploring the superposition property of a wireless multiple-access channel. This is achieved by joint device selection and beamforming design, which is modeled as a sparse and low-rank optimization problem to support efficient algorithms design. To achieve this goal, we provide a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect the fixed-rank constraint in the procedure of device selection. A DC algorithm is further developed to solve the resulting DC program with global convergence guarantees. The algorithmic advantages and admirable performance of the proposed methodologies are demonstrated through extensive numerical results.