Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior

With the increasing importance of data sharing for collaboration and innovation, it is becoming more important to ensure that data is managed and shared in a secure and trustworthy manner. Data governance is a common approach to managing data, but it faces many challenges such as data silos, data consistency, privacy, security, and access control. To address these challenges, this paper proposes a comprehensive framework that integrates data trust in federated learning with InterPlanetary File System, blockchain, and smart contracts to facilitate secure and mutually beneficial data sharing while providing incentives, access control mechanisms, and penalizing any dishonest behavior. The experimental results demonstrate that the proposed model is effective in improving the accuracy of federated learning models while ensuring the security and fairness of the data-sharing process. The research paper also presents a decentralized federated learning platform that successfully trained a CNN model on the MNIST dataset using blockchain technology. The platform enables multiple workers to train the model simultaneously while maintaining data privacy and security. The decentralized architecture and use of blockchain technology allow for efficient communication and coordination between workers. This platform has the potential to facilitate decentralized machine learning and support privacy-preserving collaboration in various domains.Comment: To appear in the 5th International Congress on Blockchain and Applications (BLOCKCHAIN'23). Publish by the Lecture Notes in Networks and Systems series of Springer Verla

100 MHz Amplitude and Polarization Modulated Optical Source for Free-Space Quantum Key Distribution at 850 nm

We report on an integrated photonic transmitter of up to 100 MHz repetition rate, which emits pulses centered at 850 nm with arbitrary amplitude and polarization. The source is suitable for free space quantum key distribution applications. The whole transmitter, with the optical and electronic components integrated, has reduced size and power consumption. In addition, the optoelectronic components forming the transmitter can be space-qualified, making it suitable for satellite and future space missions.Comment: 6 figures, 2 table

Securing personal distributed environments

The Personal Distributed Environment (PDE) is a new concept being developed by Mobile VCE allowing future mobile users flexible access to their information and services. Unlike traditional mobile communications, the PDE user no longer needs to establish his or her personal communication link solely through one subscribing network but rather a diversity of disparate devices and access technologies whenever and wherever he or she requires. Depending on the services’ availability and coverage in the location, the PDE communication configuration could be, for instance, via a mobile radio system and a wireless ad hoc network or a digital broadcast system and a fixed telephone network. This new form of communication configuration inherently imposes newer and higher security challenges relating to identity and authorising issues especially when the number of involved entities, accessible network nodes and service providers, builds up. These also include the issue of how the subscribed service and the user’s personal information can be securely and seamlessly handed over via multiple networks, all of which can be changing dynamically. Without such security, users and operators will not be prepared to trust their information to other networks

MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query it with their data via an API. However, if the user's input is sensitive, sending it to the server is undesirable and sometimes even legally not possible. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model. In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service. MLCapsule executes the model locally on the user's side and therefore the data never leaves the client. Meanwhile, MLCapsule offers the service provider the same level of control and security of its model as the commonly used server-side execution. In addition, MLCapsule is applicable to offline applications that require local execution. Beyond protecting against direct model access, we couple the secure offline deployment with defenses against advanced attacks on machine learning models such as model stealing, reverse engineering, and membership inference