Mitigating Sovereign Data Exchange Challenges: A Mapping to Apply Privacy- and Authenticity-Enhancing Technologies

• URL: http://arxiv.org/abs/2207.01513v1
• Date: Mon, 20 Jun 2022 08:16:42 GMT
• Title: Mitigating Sovereign Data Exchange Challenges: A Mapping to Apply Privacy- and Authenticity-Enhancing Technologies
• Authors: Kaja Schmidt and Gonzalo Munilla Garrido and Alexander M\"uhle and Christoph Meinel
• Abstract summary: Authenticity Enhancing Technologies (AETs) and Privacy-Enhancing Technologies (PETs) are considered to engage in Sovereign Data Exchange (SDE) PETs and AETs are technically complex, which impedes their adoption. This study empirically constructs a challenge-oriented technology mapping.
• Score: 67.34625604583208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Harmful repercussions from sharing sensitive or personal data can hamper institutions' willingness to engage in data exchange. Thus, institutions consider Authenticity Enhancing Technologies (AETs) and Privacy-Enhancing Technologies (PETs) to engage in Sovereign Data Exchange (SDE), i.e., sharing data with third parties without compromising their own or their users' data sovereignty. However, these technologies are often technically complex, which impedes their adoption. To support practitioners select PETs and AETs for SDE use cases and highlight SDE challenges researchers and practitioners should address, this study empirically constructs a challenge-oriented technology mapping. First, we compile challenges of SDE by conducting a systematic literature review and expert interviews. Second, we map PETs and AETs to the SDE challenges and identify which technologies can mitigate which challenges. We validate the mapping through investigator triangulation. Although the most critical challenge concerns data usage and access control, we find that the majority of PETs and AETs focus on data processing issues.

Related papers

• An interdisciplinary exploration of trade-offs between energy, privacy and accuracy aspects of data [0.0]
  Digital era has raised many societal challenges, including ICT's rising energy consumption and protecting privacy of personal data processing. This paper considers both aspects in relation to machine learning accuracy in an interdisciplinary exploration.
  arXiv  Detail & Related papers  (2024-09-30T10:01:14Z)
• Speech Emotion Recognition under Resource Constraints with Data Distillation [64.36799373890916]
  Speech emotion recognition (SER) plays a crucial role in human-computer interaction. The emergence of edge devices in the Internet of Things presents challenges in constructing intricate deep learning models. We propose a data distillation framework to facilitate efficient development of SER models in IoT applications.
  arXiv  Detail & Related papers  (2024-06-21T13:10:46Z)
• SoK: Demystifying Privacy Enhancing Technologies Through the Lens of Software Developers [4.171555557592296]
  In the absence of data protection measures, software applications lead to privacy breaches. This review analyses 39 empirical studies on developers' privacy practices. It reports the usage of six PETs in software application scenarios. It discusses challenges developers face when integrating PETs into software.
  arXiv  Detail & Related papers  (2023-12-30T12:24:40Z)
• KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions [14.095117843726511]
  We propose the Knowledge Enhanced Shallow and Deep Transformer(KESDT) model for ADR detection. To cope with the first issue, we incorporate the domain keywords into the Transformer model through a shallow fusion manner. To overcome the low annotated data, we integrate the synonym sets into the Transformer model through a deep fusion manner.
  arXiv  Detail & Related papers  (2023-08-18T06:10:11Z)
• Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education [1.8078387709049526]
  Transferring medical data "as open as possible" poses a risk to patient privacy. Existing regulations push towards keeping medical data "as closed as necessary" to avoid re-identification risks. This paper explores the legal regulations and terminologies for medical data-sharing.
  arXiv  Detail & Related papers  (2023-07-18T16:53:07Z)
• On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise [0.0]
  We study the challenges associated with deploying synthetic data, a subfield of Generative AI. Our focus centers on enterprise deployment, with an emphasis on privacy concerns caused by the vast amount of personal and highly sensitive data.
  arXiv  Detail & Related papers  (2023-07-09T15:42:22Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• Advanced Data Augmentation Approaches: A Comprehensive Survey and Future directions [57.30984060215482]
  We provide a background of data augmentation, a novel and comprehensive taxonomy of reviewed data augmentation techniques, and the strengths and weaknesses (wherever possible) of each technique. We also provide comprehensive results of the data augmentation effect on three popular computer vision tasks, such as image classification, object detection and semantic segmentation.
  arXiv  Detail & Related papers  (2023-01-07T11:37:32Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)
• Deep Technology Tracing for High-tech Companies [67.86308971806322]
  We develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company. DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network.
  arXiv  Detail & Related papers  (2020-01-02T07:44:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.