Decentralized learning enables collaborative machine learning with enhanced privacy by allowing participants to train models locally and share updates for aggregation instead of sharing raw data. However, such systems are vulnerable to poisoning attacks that may compromise the learning process. This threat becomes even more severe when combined with sybil attacks, where adversaries contribute numerous malicious updates with minimal effort. To overcome this challenge, particularly in the permissionless setup, we propose SyDeLP, a blockchain-enabled protocol for decentralized learning. SyDeLP integrates byzantine tolerant aggregation for poisoning mitigation with a Verifiable Delay Function to counter sybil attacks requiring Proofs of Work (PoW) to participate. Honest behavior is incentivized by dynamically reducing PoW difficulty, decreasing the computational burden for honest nodes over time. Empirical evaluations conducted on a benchmark dataset across three types of poisoning attacks demonstrate that SyDeLP consistently outperforms existing solutions in terms of resilience.

Connected vehicles generate a vast amount of data and share it with external entities such as the cloud, neighboring vehicles, Road-Side Units (RSUs), and other third-party services in a Vehicle to Everything (V2X) setting. This data is vulnerable and can lead to the leakage of personal information of vehicle owners, such as driving habits, travel routes, and identity theft, among others. Moreover, with the implementation of the General Data Protection Regulation (GDPR), it becomes imperative to empower users with control over their data and the ability to choose whom they share it with. To address this objective, we present a protocol that utilizes a combination of sticky policies and a proxy re-encryption scheme. This protocol ensures that user-defined access controls on the data persist even when crossing organizational boundaries and addresses the confidentiality, integrity, and accountability of vehicle data. Furthermore, we assess our protocol using a semi-honest threat model and analyze its vulnerabilities. Lastly, we perform a quantitative analysis of the data flow model to observe the system's performance.

Modern service-based systems are characterized by applications composed of heterogeneous services provided by multiple, untrusted providers, and deployed along the (multi-) cloud-edge continuum. This scenario of increasing pervasiveness, complexity, and multi-party service recruitment urgently calls for solutions to increase applications privacy and security, on the one hand, and guarantee that applications behave as expected and support a given set of non-functional requirements, on the other hand. Certification schemes became the widespread means to answer this call, but they still build on old-fashioned assumptions that hardly hold in today’s services world. They assume that all actors involved in a certification process are trusted "by definition", meaning that certificates are supposed to be correct and be safely usable for decision-making, such as certification-based service selection and composition. In this paper, we depart from such unrealistic assumptions and define the first certification scheme that is completely transparent to the involved actors and significantly more resistant to misbehavior (e.g., collusion). We design a blockchain-based architecture to support our scheme, redefining the actors and their roles. The quality and performance of our scheme are evaluated in a case study scenario.

International audience

Participatory sensing is an emerging paradigm in which citizens voluntarily use their mobile phones to capture and share sensed data from their surrounding environment in order to monitor and analyze some phenomena. Participating users can disrupt the system by contributing corrupted, fabricated, or erroneous data. Different reputation systems have been proposed to monitor participants' behavior and to estimate their honesty. There are some attacks that were not considered by the existing reputation systems in this context including corruption, collusion, and on-off attack. In this paper, we propose a more robust and efficient reputation system designed for these applications. Our reputation system incorporates a mechanism to defend against those attacks. Experimental results indicate that our system can tolerate up to 60% of colluding adversaries involved in the sensing campaign. This enables our system to aggregate the data more accurately compared with the state-of-the art. Moreover, the system can detect on-off attackers even if they strategically contribute some good data with high probability (e.g. 0.8).

The integration of privacy into reputation systems is a crucial need for building secure and reliable participatory sensing applications. Participants are given the assurance that their privacy is preserved even if they contribute some personal sensitive data. In addition, reputation systems allow an application server to monitor participants' behaviors and evict those who provide the system with corrupted data. However, this integration requires achieving seemingly conflicting objectives. Reputation systems monitor participants behaviors along subsequent interactions. Whereas, one of the major objectives of privacy preserving systems is to unlink subsequent interactions. In this paper, we define a new attack (RR attack), which exploits this conflict in order to detect the succession of contributions provided by the same participant and to subsequently re-identify his original identity. We show that using this attack, more than 35% of contributions can be associated to their successive contributions in each campaign. We then propose PrivaSense as a new privacy preserving reputation system that integrates both reputation and privacy such that their objectives are simultaneously achieved. Experimental results are conducted using a real data-set. These results show that PrivaSense decreases by up to 80% the number of contributions linked to their original providers.

OBJECTIVE: As healthcare systems and providers move toward meaningful use of electronic health records, longitudinal care plans (LCPs) may provide a means to improve communication and coordination as patients transition across settings. The objective of this study was to determine the current state of communication of LCPs across settings and levels of care. MATERIALS AND METHODS: We conducted surveys and interviews with professionals from emergency departments, acute care hospitals, skilled nursing facilities, and home health agency settings in six regions in the USA. We coded the transcripts according to the Agency for Healthcare Research and Quality (AHRQ) 'Broad Approaches' to care coordination to understand the degree to which current practice meets the definition of an LCP. RESULTS: Participants (n=22) from all settings reported that LCPs do not exist in their current state. We found LCPs in practice, and none of these were shared or reconciled across settings. Moreover, we found wide variation in the types and formats of care plan information that was communicated as patients transitioned. The most common formats, even when care plan information was communicated within the same healthcare system, were paper and fax. DISCUSSION: These findings have implications for data reuse, interoperability, and achieving widespread adoption of LCPs. CONCLUSIONS: The use of LCPs to support care transitions is suboptimal. Strategies are needed to transform the LCP from vision to reality.

A prediction-based routing protocol for mobile delay tolerant networks functions by forwarding a message from one intermediate node to another if the latter has higher probability of encountering the destination node. However, this process compromises the privacy of the nodes by revealing their mobility patterns. In this paper, we propose a privacy preserving prediction-based routing protocol that forwards messages by comparing information about communities of nodes instead of individual nodes. Specifically, it compares the maximum probability that a node in the community of a potential intermediate node will encounter the destination node. We present theoretical security analyses as well as practical performance evaluations. Our simulations on a well established community-based mobility model demonstrate that our protocol has comparable performance to existing prediction-based protocols. Yet our protocol is the only one that preserves the privacy of nodes.

Users hesitate to submit negative feedback in reputation systems due to the fear of retaliation from the recipient user. A privacy preserving reputation protocol protects users by hiding their individual feedback and revealing only the reputation score. We present a privacy preserving reputation protocol for the malicious adversarial model. The malicious users in this model actively attempt to learn the private feedback values of honest users as well as to disrupt the protocol. Our protocol does not require centralized entities, trusted third parties, or specialized platforms, such as anonymous networks and trusted hardware. Moreover, our protocol is efficient. It requires an exchange of messages, where and are the number of users in the protocol and the environment, respectively.

This paper presents a road map for the design and development of sustainable hybrid Field Programmable Gate Array (FPGA) controlled High Brightness Light Emitting Diode (HB LED) illumination systems. The proposed hybrid system design presents the foundation for future sustainable high efficiency illumination systems. In this system design, an FPGA is used to individually control an array of HB LEDs. Each HB LED can operate in a different mode defined by the user. Photovoltaic (PV) panels-based systems are used as the primary source of energy in this system while the electric grid is used as the backup source that supplies power to the HB LEDs only when the power of the PV system is not sufficient to supply all the system HB LEDs and the PV system batteries are depleted below the minimum level set in the system design. This new hybrid design should serve as an important link between the current AC-based illumination systems and future sustainable DC-based solar illumination systems. Since computer simulation is an important tool for future illumination system design and analysis, it is presented to assist designers in studying different designs and in optimizing the one to be implemented.

This paper presents a case study of designing an e-learning course in the field of electrical engineering-Industrial automation program at Palestine Polytechnic University. The applied methodology consisting of few stages starting with formulating the major course objectives, extracting topics outcomes, design the appropriate teaching tools and storyboard, and as final stage the evaluation system is designed.

An online simulated Smith chart is designed as an aid to train students to perform impedance matching exercises and to easily follow amplifier design procedures. The tool enables the instructor to conduct classes remotely to a group of students in a broadcasting mode. Students have participated actively and regularly in the online Smith chart tool and their reactions have been positive.

The secure sum protocol is a well-known protocol for computing the sum of private inputs from distributed entities such that the inputs remain private. In this paper we present protocols for computing reputation in a privacy preserving manner that are inspired by the secure sum protocol. We provide a protocol that is secure under the semi-honest adversarial model as well as one that is secure under the stronger non-disruptive malicious model. Although the protocols are inspired by secure sum, they do not suffer from the issues that plague secure sum. Our protocols are resilient against colluding entities, which secure sum is not. The protocols that we develop are also efficient. We require an exchange of O(n) messages under the semi-honest model, where n is the number of feedback providers in the protocol. This is the same complexity offered by secure sum. Our protocol for the non-disruptive malicious model exchanges O(N log n) messages, where N is the number of entities in the system.