492 research outputs found
CrossFlow: Cross-Organizational Workflow Management for Service Outsourcing in Dynamic Virtual Enterprises
In this report, we present the approach to cross-organizational workflow management of the CrossFlow project. CrossFlow is a European research project aiming at the support of cross-organizational workflows in dynamic virtual enterprises. The cooperation in these virtual enterprises is based on dynamic service outsourcing specified in electronic contracts. Service enactment is performed by dynamically linking the workflow management infrastructures of the involved organizations. Extended service enactment support is provided in the form of cross-organizational transaction management and process control, advanced quality of service monitoring, and support for high-level flexibility in service enactment. CrossFlow technology is realized on top of a commercial workflow management platform and applied in two real-world scenarios in the contexts of a logistics and an insurance company
Web Services Support for Dynamic Business Process Outsourcing
Outsourcing of business processes is crucial for organizations to be effective, efficient and flexible. To meet fast-changing market conditions, dynamic outsourcing is required, in which business relationships are established and enacted on-the-fly in an adaptive, fine-grained way unrestricted by geographic distance. This requires automated means for both the establishment of outsourcing relationships and for the enactment of services performed in these relationships over electronic channels. Due to wide industry support and the underlying model of loose coupling of services, Web services increasingly become the mechanism of choice to connect organizations across organizational boundaries. This paper analyzes to which extent Web services support the dynamic process outsourcing paradigm. We discuss contract -based dynamic business process outsourcing to define requirements and then introduce the Web services framework. Based on this, we investigate the match between the two. We observe that the Web services framework requires further support for cross - organizational business processes and mechanisms for contracting, QoS management and process-based transaction support and suggest ways to fill those gaps
Modeling IoT-aware Business Processes - A State of the Art Report
This research report presents an analysis of the state of the art of modeling
Internet of Things (IoT)-aware business processes. IOT links the physical world
to the digital world. Traditionally, we would find information about events and
processes in the physical world in the digital world entered by humans and
humans using this information to control the physical world. In the IoT
paradigm, the physical world is equipped with sensors and actuators to create a
direct link with the digital world. Business processes are used to coordinate a
complex environment including multiple actors for a common goal, typically in
the context of administrative work. In the past few years, we have seen
research efforts on the possibilities to model IoT- aware business processes,
extending process coordination to real world entities directly. This set of
research efforts is relatively small when compared to the overall research
effort into the IoT and much of the work is still in the early research stage.
To create a basis for a bridge between IoT and BPM, the goal of this report is
to collect and analyze the state of the art of existing frameworks for modeling
IoT-aware business processes.Comment: 42 page
A Hybrid Approach to Privacy-Preserving Federated Learning
Federated learning facilitates the collaborative training of models without
the sharing of raw data. However, recent attacks demonstrate that simply
maintaining data locality during training processes does not provide sufficient
privacy guarantees. Rather, we need a federated learning system capable of
preventing inference over both the messages exchanged during training and the
final trained model while ensuring the resulting model also has acceptable
predictive accuracy. Existing federated learning approaches either use secure
multiparty computation (SMC) which is vulnerable to inference or differential
privacy which can lead to low accuracy given a large number of parties with
relatively small amounts of data each. In this paper, we present an alternative
approach that utilizes both differential privacy and SMC to balance these
trade-offs. Combining differential privacy with secure multiparty computation
enables us to reduce the growth of noise injection as the number of parties
increases without sacrificing privacy while maintaining a pre-defined rate of
trust. Our system is therefore a scalable approach that protects against
inference threats and produces models with high accuracy. Additionally, our
system can be used to train a variety of machine learning models, which we
validate with experimental results on 3 different machine learning algorithms.
Our experiments demonstrate that our approach out-performs state of the art
solutions
CrossFlow: Cross-Organizational Workflow Management in Dynamic Virtual Enterprises
In this report, we present the approach to cross-organizational workflow\ud
management of the CrossFlow project. CrossFlow is a European research\ud
project aiming at the support of cross-organizational workflows in dynamic\ud
virtual enterprises. The cooperation in these virtual enterprises is based on\ud
dynamic service outsourcing specified in electronic contracts. Service enactment\ud
is performed by dynamically linking the workflow management infrastructures\ud
of the involved organizations. Extended service enactment support is provided in the form of cross-organizational transaction management and process control, advanced quality of service monitoring, and support for high-level flexibility in service enactment. CrossFlow technology is realized on top of a commercial workflow management platform and applied in two real-world scenarios in the contexts of a logistics and an insurance company
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
Federated learning has emerged as a promising approach for collaborative and
privacy-preserving learning. Participants in a federated learning process
cooperatively train a model by exchanging model parameters instead of the
actual training data, which they might want to keep private. However, parameter
interaction and the resulting model still might disclose information about the
training data used. To address these privacy concerns, several approaches have
been proposed based on differential privacy and secure multiparty computation
(SMC), among others. They often result in large communication overhead and slow
training time. In this paper, we propose HybridAlpha, an approach for
privacy-preserving federated learning employing an SMC protocol based on
functional encryption. This protocol is simple, efficient and resilient to
participants dropping out. We evaluate our approach regarding the training time
and data volume exchanged using a federated learning process to train a CNN on
the MNIST data set. Evaluation against existing crypto-based SMC solutions
shows that HybridAlpha can reduce the training time by 68% and data transfer
volume by 92% on average while providing the same model performance and privacy
guarantees as the existing solutions.Comment: 12 pages, AISec 201
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