As an essential element of the next generation Internet, Internet of Things (IoT) has been undergoing an extensive development in recent years. In addition to the enhancement of people's daily lives, ...IoT devices also generate/gather a massive amount of data that could be utilized by machine learning and big data analytics for different applications. Due to the machine-to-machine communication nature of IoT, data security and privacy are crucial issues that must be addressed to prevent different cyber attacks (e.g., impersonation and data pollution/poisoning attacks). Nevertheless, due to the constrained computation power and the diversity of IoT devices, it is a challenging problem to develop lightweight and versatile IoT security solutions. In this article, we propose an efficient, secure, and privacy-preserving message authentication scheme for IoT. Our scheme supports IoT devices with different cryptographic configurations and allows offline/online computation, making it more versatile and efficient than the previous solutions.
Recently, the study of road surface condition monitoring has drawn great attention to improve the traffic efficiency and road safety. As a matter of fact, this activity plays a critical role in the ...management of the transportation infrastructure. Trustworthiness and individual privacy affect the practical deployment of the vehicular crowdsensing network. Mobile sensing as well as the contemporary applications are made use of problem solving. The fog computing paradigm is introduced to meet specific requirements, including the mobility support, low latency, and location awareness. The fog-based vehicular crowdsensing network is an emerging transportation management infrastructure. Moreover, the fog computing is effective to reduce the latency and improve the quality of service. Most of the existing authentication protocols cannot help the drivers to judge a message when the authentication on the message is anonymous. In this paper, a fog-based privacy-preserving scheme is proposed to enhance the security of the vehicular crowdsensing network. Our scheme is secure with the security properties, including non-deniability, mutual authentication, integrity, forward privacy, and strong anonymity. We further analyze the designed scheme, which can not only guarantee the security requirements but also achieve higher efficiency with regards to computation and communication compared with the existing schemes.
Industrial Internet of Things (IIoT) facilitate private data collecting via (a broad range of) sensors, and the analysis of such data can inform decision making at different levels. Federated ...learning (FL) can be used to analyze the collected data, in privacy-preserving manner by transmitting model updates instead of private data in IIoT networks. The FL framework is, however, vulnerable because model updates are easily tampered with by malicious agents. Motivated by this observation, we propose a novel chameleon hash scheme with a changeable trapdoor (CHCT) for secure FL in IIoT settings. Our scheme imposes various constraints on the use of trapdoor. We give a rigorous security analysis on our CHCT scheme. We also instantiate the CHCT scheme as a redactable medical blockchain (RMB). The experimental evaluations demonstrate the practical utility of CHCT in terms of accuracy and efficiency.
In this paper, we introduce a new cryptographic primitive named Designated Verifier Proxy Re-Signature (DVPRS). Different from a normal proxy re-signature, our DVPRS is defined based on the notion of ...Designated Verifier Signature (DVS) which is very useful in many applications that require “deniable authentication”. Since a DVS can only be verified by a designated verifier, in addition to the re-sign algorithm which allows a proxy to use a resign key to change the signer of a DVS on a message, we also define the re-designate-verifier algorithm for DVPRS which allows a proxy to change the designated verifier of a DVS. We present the formal definition, security model, and an efficient construction of DVPRS, and prove its security under some standard assumptions. We show that DVPRS is very useful in many communication and network applications that require deniable and/or anonymous authentication.
In a recent paper (IEEE Trans. Wireless Commun., vol. 14, no. 1, 2015), He et al. proposed an accountable and privacy-enhanced access control (APAC) protocol, which aimed to provide privacy for ...honest users against network owners and accountability against misbehaving users without the involvement of any trusted third party. However, the level of trust on the network owner has not been clearly defined in He et al.'s paper, and we demonstrate in this letter that in the case where the network owners cannot be trusted to correctly generate the system parameters, then the APAC protocol cannot ensure user privacy.
In this article, a driver model–based direct yaw moment controller, selected as the upper controller, is developed, of which the control target is determined through a reference driver model in ...accordance with the driver’s intention. The sliding surface is chosen by the difference between the desired yaw rate and the real output yaw rate. Then, the desired yaw moment is calculated by the sliding mode control. In the lower controller, a novel control torque distribution strategy is designed based on the analysis of the tire characteristics. In addition, an admissible control set of the control torques is calculated in real time through an embedded tire model “UniTire.” Finally, a driver-in-the-loop experiment, via the driving simulator, is conducted to verify the proposed direct yaw moment controller.
•Securely and efficiently harnessing distributed knowledge in machine learning.•Lightweight secure aggregation of teacher ensembles based on secret sharing.•Differential privacy guarantees for ...individual participants contributing knowledge.
Training high-performing machine learning models require a rich amount of data which is usually distributed among multiple data sources in practice. Simply centralizing these multi-sourced data for training would raise critical security and privacy concerns, and might be prohibited given the increasingly strict data regulations. To resolve the tension between privacy and data utilization in distributed learning, a machine learning framework called private aggregation of teacher ensembles (PATE) has been recently proposed. PATE harnesses the knowledge (label predictions for an unlabeled dataset) from distributed teacher models to train a student model, obviating access to distributed datasets. Despite being enticing, PATE does not offer protection for the individual label predictions from teacher models, which still entails privacy risks. In this paper, we propose SEDML, a new protocol which allows to securely and efficiently harness the distributed knowledge in machine learning. SEDML builds on lightweight cryptography and provides strong protection for the individual label predictions, as well as differential privacy guarantees on the aggregation results. Extensive evaluations show that while providing privacy protection, SEDML preserves the accuracy as in the plaintext baseline. Meanwhile, SEDML outperforms the state-of-the-art work of Xiang et al. (ICDCS’20) by 43× in computation and 1.23× in communication.
Idiopathic granulomatous mastitis (IGM) is an uncommon, chronic inflammatory breast disease with elusive aetiology, simulating malignancy clinically and radiologically. Here we present our 10-year ...review on a region-wide multicentre IGM database. A retrospective study was performed on a prospectively maintained database from three University affiliated hospitals in Hong Kong and Shenzhen, China. All patients with biopsy proven IGM were included while patients with positive culture of Mycobacterium tuberculosis were excluded. Disease recurrence rate and its prognosticators were evaluated. A total of 102 patients were included between January 2007 and December 2017. Median age was 33 years (range 20–54). Most patients presented with painful inflammatory mass (n = 57); median size at presentation was 37 mm (6–92 mm). Sixty-three patients had bacterial culture performed on the pus sample: eight patients had Corynebacterium kroppenstedtii while four had Corynebacterium species not otherwise specified. Seventy-seven (75.5%) patients received conservative treatment with oral corticosteroid (±antibiotics) and drainage only, while 25 (24.5%) patients received breast lump excision after initial medical treatment. Twelve (11.8%) patients developed recurrence after a median follow-up interval of 14 months (4–51 months). Univariate analysis revealed that abscess on presentation, history of smoking, and presence of C. kroppenstedtii were significant prognosticators for recurrence. Subsequent multivariate analysis with logistic regression revealed cigarette smoking and isolation of C. kroppenstedtii as independent risk factors for disease recurrence (p < 0.05). In conclusion, IGM is uncommon with a recurrence rate of 12%, especially in patients with history of smoking and isolation of C. kroppenstedtii.
Training high-performing deep learning models require a rich amount of data which is usually distributed among multiple data sources in practice. Simply centralizing these multi-sourced data for ...training would raise critical security and privacy concerns, and might be prohibited given the increasingly strict data regulations. To resolve the tension between privacy and data utilization in distributed learning, a machine learning framework called private aggregation of teacher ensembles(PATE) has been recently proposed. PATE harnesses the knowledge (label predictions for an unlabeled dataset) from distributed teacher models to train a student model, obviating access to distributed datasets. Despite being enticing, PATE does not offer protection for the individual label predictions from teacher models, which still entails privacy risks. In this paper, we propose SEDML, a new protocol which allows to securely and efficiently harness the distributed knowledge in machine learning. SEDML builds on lightweight cryptography and provides strong protection for the individual label predictions, as well as differential privacy guarantees on the aggregation results. Extensive evaluations show that while providing privacy protection, SEDML preserves the accuracy as in the plaintext baseline. Meanwhile, SEDML's performance in computing and communication is 43 times and 1.23 times higher than the latest technology, respectively.
The presence of malignant ductal carcinoma within phyllodes tumor is being underestimated, underdiagnosed, and underreported. However, the presence of ductal carcinoma is clinically relevant as it ...may alter clinical judgment and management. Here, we report 6 cases of ductal carcinoma (5 ductal carcinoma in situ and 1 invasive ductal carcinoma) arising from phyllodes tumor from a large multi-center database in Hong Kong and China.
Phyllodes tumor (PT) is an uncommon fibroepithelial tumor of the breast showing predominately proliferation of the stromal component. The presence of ductal carcinoma in situ (DCIS) or invasive ductal carcinoma is rare, with only a few cases reported in the literature.
A retrospective review of a prospectively maintained database was performed. Patients who were treated for PT in 5 hospitals in Hong Kong and Shenzhen, China over a period of 20 years (1997-2016) were evaluated. All pathology slides were reported by specialist pathologists. Patients with coexisting ductal carcinoma were identified.
A total of 557 patients were included in this cohort; 363 (65.2%) patients had benign PT, 130 (23.3%) had borderline PT, and 64 (11.5%) had malignant PT. There were 6 (1.1%) patients with coexisting ductal carcinoma in the PT; 5 were DCIS and 1 was invasive ductal carcinoma. The median age was 46.5 years (range, 25-54 years). Ductal carcinoma occurred more frequently in malignant PT than in benign or borderline PT (4.7% vs. 0.6%; P = .02). However, malignant PT was not associated with higher DCIS grade (P = .1). All patients underwent surgery with clear resection margins. After a median follow-up interval of 70 months (range, 2-101 months), all patients remained disease- and recurrence-free.
We report 6 additional uncommon cases of ductal carcinoma complicating PT. The presence of ductal carcinoma was not adverse prognosticator as these are usually incidental and situated within the harboring PT.
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