MOTIVATION: Methods for sharing gene regulatory information and knowledge on FAIR principles, particularly in the context of tissue-specific gene regulation, remain poorly defined and implemented, hampering discovery and clinical genetic diagnosis. RESULTS: We specified FAIR principles for tissue-specific gene regulatory information and knowledge; implemented them by developing a registry of regulatory elements and aggregating FAIR gene regulatory information from several major sources; developed computational tools that utilize these FAIR resources; and demonstrated their utility by associating gene regulatory variants with major subtypes of congenital heart disease. AVAILABILITY AND IMPLEMENTATION: Variant prioritization infrastructure tools are available in genboree node repository at https://genboree.org/verdaccio/#/. Detailed documentation is available at https://ldh.clinicalgenome.org/docs/ldh/overview.html#related-services. The code for use case analyses and free access variant data is available on Zenodo with DOI: https://doi.org/10.5281/zenodo.17833070.

The network traffic data produced by the Internet of Things (IoT) devices are collected by Internet Service Providers (ISPs) and IoT manufacturers, and often also shared with their third parties to maintain and enhance user experiences. Unfortunately, on-path adversaries could fingerprint users’ sensitive and private information by analyzing these network traffic traces. While there’s a growing body of literature on defending against this side-channel attack—malicious IoT traffic analytics (TA), there’s currently no systematic method to compare and evaluate the comprehensiveness of these existing studies. To address this problem, we design a new low-cost, open-source system framework—IoT Traffic Exposure Monitoring Toolkit (ITEMTK) that enables people to comprehensively examine and validate prior attack models and their defending approaches. During the design of ITEMTK, we also identified a novel image-based attack capable of inferring sensitive user information, even when users implement the most robust preventative measures in their smart homes. We then present a new, initial, prototype-level defense mechanism—PrivacyProtector, which could effectively defend against a wide range of state-of-the-art machine learning-based TA attacks, including our newly proposed image-based TA attacks. ITEMTK’s flexibility allows users’ easy expansion by integrating new TA attack models and defenses to benchmark their future work.

Transforming user content engagement into citizenship behaviours is significant for a social media platform’s prosperity. By integrating psychological ownership theory with the technology acceptance model, this study examined how and when in the context of social media, three different types of user content engagement (consumption, participation, and production) impact three different types of user citizenship behaviours (recommendation, helping, and feedback). Building upon survey data collected from 401 social media users in China, partial least squares structural equation modelling (PLS-SEM) was used to demonstrate that user content engagement is positively linked to user citizenship behaviours through the mediation of psychological ownership. The positive association between psychological ownership and recommendation behaviour is enhanced by perceived ease of use but weakened by perceived usefulness. Conversely, perceived ease of use weakens the positive link between psychological ownership and helping behaviour, while this relationship is enhanced by perceived usefulness. The positive effect of psychological ownership on feedback behaviour is weakened by perceived ease of use. This study contributes to the literature by revealing the transformative process from user content engagement to user citizenship behaviours on social media platforms.

The Playbook Workflow Builder (PWB) is a web-based platform to dynamically construct and execute bioinformatics workflows by utilizing a growing network of input datasets, semantically annotated API endpoints, and data visualization tools contributed by an ecosystem of collaborators. Via a user-friendly user interface, workflows can be constructed from contributed building-blocks without technical expertise. The output of each step of the workflow is added into reports containing textual descriptions, figures, tables, and references. To construct workflows, users can click on cards that represent each step in a workflow, or construct workflows via a chat interface that is assisted by a large language model (LLM). Completed workflows are compatible with Common Workflow Language (CWL) and can be published as research publications, slideshows, and posters. To demonstrate how the PWB generates meaningful hypotheses that draw knowledge from across multiple resources, we present several use cases. For example, one of these use cases prioritizes drug targets for individual cancer patients using data from the NIH Common Fund programs GTEx, LINCS, Metabolomics, GlyGen, and ExRNA. The workflows created with PWB can be repurposed to tackle similar use cases using different inputs. The PWB platform is available from: https://playbook-workflow-builder.cloud/.

Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new low-cost, open source user-centric defense system—PrivacyGuard—that enables people to regain the privacy leakage control of their IoT devices while still permitting sophisticated IoT data analytics that is necessary for smart home automation. In essence, our approach employs intelligent deep convolutional generative adversarial network assisted IoT device traffic signature learning, long short-term memory based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from five smart homes and buildings. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art adversarial machine learning and deep learning based user in-home activity inference and fingerprinting attacks and help users achieve the balance between their IoT data utility and privacy preserving.

The Internet of Things (IoT) devices have been increasingly deployed in smart homes for automation. Unfortunately, extensive recent research shows that external on-path adversaries can infer and fingerprint user sensitive in-home activities by analyzing IoT network traffic rates alone. Most recent traffic padding-based defending approaches cannot sufficiently protect user privacy with reasonable traffic overhead. In addition, these approaches typically assume the installation of additional hub hardware in smart homes to host their traffic padding-based defending approaches. To address these problems, we design a new open-source traffic reshaping system—privacy as a router operating system service (PAROS) that enables smart home users to significantly reduce private information leaked through IoT network traffic rates. PAROS does not assume the installation of any additional hardware device. We evaluate PAROS on open-source router Operating System (OS)—OpenWrt enabled virtual machine and also two real best-selling home routers. We find that PAROS can effectively prevent a wide range of state-of-the-art adversarial machine learning-based user in-home activity inference attacks, with near-zero system overhead increasing.

The Internet of Things (IoT) devices have been increasingly deployed in smart homes and smart buildings to monitor and control their environments. The Internet traffic data produced by these IoT devices are collected by Internet Service Providers (ISPs) and IoT device manufacturers, and often shared with third-parties to maintain and enhance user services. Unfortunately, extensive recent research has shown that on-path adversaries can infer and fingerprint users' sensitive privacy information such as occupancy and user in-home activities by analyzing IoT network traffic traces. Most recent approaches that aim at defending against these malicious IoT traffic analytics can not sufficiently protect user privacy with reasonable traffic overhead. In particular, many approaches did not consider practical limitations, e.g., network bandwidth, maximum package injection rate or actual user in-home behavior in their design.