210 research outputs found
Securing Cyber-Physical Social Interactions on Wrist-worn Devices
Since ancient Greece, handshaking has been commonly practiced between two people as a friendly gesture to express trust and respect, or form a mutual agreement. In this article, we show that such physical contact can be used to bootstrap secure cyber contact between the smart devices worn by users. The key observation is that during handshaking, although belonged to two different users, the two hands involved in the shaking events are often rigidly connected, and therefore exhibit very similar motion patterns. We propose a novel key generation system, which harvests motion data during user handshaking from the wrist-worn smart devices such as smartwatches or fitness bands, and exploits the matching motion patterns to generate symmetric keys on both parties. The generated keys can be then used to establish a secure communication channel for exchanging data between devices. This provides a much more natural and user-friendly alternative for many applications, e.g., exchanging/sharing contact details, friending on social networks, or even making payments, since it doesn’t involve extra bespoke hardware, nor require the users to perform pre-defined gestures. We implement the proposed key generation system on off-the-shelf smartwatches, and extensive evaluation shows that it can reliably generate 128-bit symmetric keys just after around 1s of handshaking (with success rate >99%), and is resilient to different types of attacks including impersonate mimicking attacks, impersonate passive attacks, or eavesdropping attacks. Specifically, for real-time impersonate mimicking attacks, in our experiments, the Equal Error Rate (EER) is only 1.6% on average. We also show that the proposed key generation system can be extremely lightweight and is able to run in-situ on the resource-constrained smartwatches without incurring excessive resource consumption
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Owing to the nature of privacy protection, federated recommender systems
(FedRecs) have garnered increasing interest in the realm of on-device
recommender systems. However, most existing FedRecs only allow participating
clients to collaboratively train a recommendation model of the same public
parameter size. Training a model of the same size for all clients can lead to
suboptimal performance since clients possess varying resources. For example,
clients with limited training data may prefer to train a smaller recommendation
model to avoid excessive data consumption, while clients with sufficient data
would benefit from a larger model to achieve higher recommendation accuracy. To
address the above challenge, this paper introduces HeteFedRec, a novel FedRec
framework that enables the assignment of personalized model sizes to
participants. In HeteFedRec, we present a heterogeneous recommendation model
aggregation strategy, including a unified dual-task learning mechanism and a
dimensional decorrelation regularization, to allow knowledge aggregation among
recommender models of different sizes. Additionally, a relation-based ensemble
knowledge distillation method is proposed to effectively distil knowledge from
heterogeneous item embeddings. Extensive experiments conducted on three
real-world recommendation datasets demonstrate the effectiveness and efficiency
of HeteFedRec in training federated recommender systems under heterogeneous
settings
Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
Federated recommender systems (FedRecs) have gained significant attention for
their potential to protect user's privacy by keeping user privacy data locally
and only communicating model parameters/gradients to the server. Nevertheless,
the currently existing architecture of FedRecs assumes that all users have the
same 0-privacy budget, i.e., they do not upload any data to the server, thus
overlooking those users who are less concerned about privacy and are willing to
upload data to get a better recommendation service. To bridge this gap, this
paper explores a user-governed data contribution federated recommendation
architecture where users are free to take control of whether they share data
and the proportion of data they share to the server. To this end, this paper
presents a cloud-device collaborative graph neural network federated
recommendation model, named CDCGNNFed. It trains user-centric ego graphs
locally, and high-order graphs based on user-shared data in the server in a
collaborative manner via contrastive learning. Furthermore, a graph mending
strategy is utilized to predict missing links in the graph on the server, thus
leveraging the capabilities of graph neural networks over high-order graphs.
Extensive experiments were conducted on two public datasets, and the results
demonstrate the effectiveness of the proposed method
Impacts of Future Climate Change on Net Primary Productivity of Grassland in Inner Mongolia, China
Net Primary Productivity (NPP) of grassland is a key variable of terrestrial ecosystems and is an important parameter for characterizing carbon cycles in grassland ecosystems. In this research, the Inner Mongolia grassland NPP was calculated using the Miami Model and the impact of climate change on grassland NPP was subsequently analyzed under the Special Report on Emissions Scenarios (SRES) A2, B2, and A1B scenarios, which are inferred from Providing Regional Climates for Impacts Studies (PRECIS) climate model system. The results showed that: (1) the NPP associated with these three scenarios had a similar distribution in Inner Mongolia: the grassland NPP increased gradually from the western region, with less than 200 g/m2/yr, to the southeast region, with more than 800 g/m2/yr. Precipitation was the main factor determining the grassland NPP; (2) compared with the baseline (1961-1990), there would be an overall increase in grassland NPP during three time periods (2020s: 2011-2040, 2050s: 2041-2070, and 2080s: 2071-2100) under the A2 and B2 scenarios; (3) under the A1B scenario, there will be a decreasing trend at middle-west region during the 2020s and 2050s; while there will be a very significant decrease from the 2050s to 2080s for middle Inner Mongolia; and (4) grassland NPP under the A1B scenario would present the most significant increase among the three scenarios, and would have the least significant increase under the B2 scenario
Parathyroid hormone-related protein serves as a prognostic indicator in oral squamous cell carcinoma
- …