A Reliable Data Provenance and Privacy Preservation Architecture for Business-Driven Cyber-Physical Systems Using Blockchain

Cyber-physical systems (CPS) including power systems, transportation, industrial control systems, etc. support both advanced control and communications among system components. Frequent data operations could introduce random failures and malicious attacks or even bring down the whole system. The dependency on a central authority increases the risk of single point of failure. To establish an immutable data provenance scheme for CPS, the authors adopt blockchain and propose a decentralized architecture to assure data integrity. In business-driven CPS, end users are required to share their personal information with multiple third parties. To prevent data leakage and preserve user privacy, the authors isolate and feed different information retrieval requests using tokens specifically generated for each type of request. Providing both traceability of data operations, and unlinkability of end user activities, a robust blockchain-based CPS is prototyped. Evaluation indicates the architecture is capable of assured data provenance validation and user privacy preservation at a low overhead

Unlocking the potential of public sector information with Semantic Web technology

Governments often hold very rich data and whilst much of this information is published and available for re-use by others, it is often trapped by poor data structures, locked up in legacy data formats or in fragmented databases. One of the great benefits that Semantic Web (SW) technology offers is facilitating the large scale integration and sharing of distributed data sources. At the heart of information policy in the UK, the Office of Public Sector Information (OPSI) is the part of the UK government charged with enabling the greater re-use of public sector information. This paper describes the actions, findings, and lessons learnt from a pilot study, involving several parts of government and the public sector. The aim was to show to government how they can adopt SW technology for the dissemination, sharing and use of its data

Requirements for Provenance on the Web

From where did this tweet originate? Was this quote from the New York Times modified? Daily, we rely on data from the Web but often it is difficult or impossible to determine where it came from or how it was produced. This lack of provenance is particularly evident when people and systems deal with Web information or with any environment where information comes from sources of varying quality. Provenance is not captured pervasively in information systems. There are major technical, social, and economic impediments that stand in the way of using provenance effectively. This paper synthesizes requirements for provenance on the Web for a number of dimensions focusing on three key aspects of provenance: the content of provenance, the management of provenance records, and the uses of provenance information. To illustrate these requirements, we use three synthesized scenarios that encompass provenance problems faced by Web users toda

Labeling Workflow Views with Fine-Grained Dependencies

This paper considers the problem of efficiently answering reachability queries over views of provenance graphs, derived from executions of workflows that may include recursion. Such views include composite modules and model fine-grained dependencies between module inputs and outputs. A novel view-adaptive dynamic labeling scheme is developed for efficient query evaluation, in which view specifications are labeled statically (i.e. as they are created) and data items are labeled dynamically as they are produced during a workflow execution. Although the combination of fine-grained dependencies and recursive workflows entail, in general, long (linear-size) data labels, we show that for a large natural class of workflows and views, labels are compact (logarithmic-size) and reachability queries can be evaluated in constant time. Experimental results demonstrate the benefit of this approach over the state-of-the-art technique when applied for labeling multiple views.Comment: VLDB201

Processing to Protect Privacy and Promote Access: A Study of Archival Processing in Medical Archives, Health Sciences Collections, and History of Medicine Collections

This research reports on the findings of a study of archival processing in medical center archives, health sciences collections, and history of medicine collections. This exploratory study examined how archivists in these settings process collections and, in so doing, how they balance the potentially conflicting needs of protecting privacy and providing timely access. Four practicing archivists were interviewed, the interviews were transcribed, and data were coded inductively. Participants addressed how they identified sensitive information scattered throughout collections, the impact this sensitive information had on processing decisions, how they communicated access restrictions, and ways in which they managed access. The findings suggest that sensitive information is best protected when it becomes a shared commitment and a shared responsibility between all groups involved.Master of Science in Library Scienc

Social Metaverse: Challenges and Solutions

Social metaverse is a shared digital space combining a series of interconnected virtual worlds for users to play, shop, work, and socialize. In parallel with the advances of artificial intelligence (AI) and growing awareness of data privacy concerns, federated learning (FL) is promoted as a paradigm shift towards privacy-preserving AI-empowered social metaverse. However, challenges including privacy-utility tradeoff, learning reliability, and AI model thefts hinder the deployment of FL in real metaverse applications. In this paper, we exploit the pervasive social ties among users/avatars to advance a social-aware hierarchical FL framework, i.e., SocialFL for a better privacy-utility tradeoff in the social metaverse. Then, an aggregator-free robust FL mechanism based on blockchain is devised with a new block structure and an improved consensus protocol featured with on/off-chain collaboration. Furthermore, based on smart contracts and digital watermarks, an automatic federated AI (FedAI) model ownership provenance mechanism is designed to prevent AI model thefts and collusive avatars in social metaverse. Experimental findings validate the feasibility and effectiveness of proposed framework. Finally, we envision promising future research directions in this emerging area.Comment: Accepted by Internet of Things Magazine in 23-May 202