The IoT devices deployed in various application scenarios will generate massive data with immeasurable value every day. These data often contain the user’s personal privacy information, so there is an imperative need to guarantee the reliability and security of IoT data sharing. We proposed a new encrypted data storing and sharing architecture by combining proxy re-encryption with blockchain technology. The consensus mechanism based on threshold proxy re-encryption eliminates dependence on the third-party central service providers. Multiple consensus nodes in the blockchain network act as proxy service nodes to re-encrypt data and combine converted ciphertext, and personal information will not be disclosed in the whole procedure. That eliminates the restrictions of using decentralized network to store and distribute private encrypted data safely. We implemented a lot of simulated experiments to evaluate the performance of the proposed framework. The results show that the proposed architecture can meet the extensive data access demands and increase a tolerable time latency. Our scheme is one of the essays to utilize the threshold proxy re-encryption and blockchain consensus algorithm to support IoT data sharing.

A Threshold Proxy Re-Encryption Scheme for Secure IoT Data Sharing Based on Blockchain

http://www.cell.com/article/S240584402200559X/pdf

The effect of trust and its antecedents towards determining users’ behavioral intention with voice-based consumer electronic devices

We propose Deep Patch Visual Odometry (DPVO), a new deep learning system for monocular Visual Odometry (VO). DPVO uses a novel recurrent network architecture designed for tracking image patches across time. Recent approaches to VO have significantly improved the state-of-the-art accuracy by using deep networks to predict dense flow between video frames. However, using dense flow incurs a large computational cost, making these previous methods impractical for many use cases. Despite this, it has been assumed that dense flow is important as it provides additional redundancy against incorrect matches. DPVO disproves this assumption, showing that it is possible to get the best accuracy and efficiency by exploiting the advantages of sparse patch-based matching over dense flow. DPVO introduces a novel recurrent update operator for patch based correspondence coupled with differentiable bundle adjustment. On Standard benchmarks, DPVO outperforms all prior work, including the learning-based state-of-the-art VO-system (DROID) using a third of the memory while running 3x faster on average. Code is available at https://github.com/princeton-vl/DPVO

Deep Patch Visual Odometry

3D data computation and visualization

3D spatial measurement for model reconstruction: A review

RGB-Fusion: Monocular 3D reconstruction with learned depth prediction

A staggering number of consumer Internet of things devices are being deployed in various application scenarios, and massive data will be generated per day. How to achieve a secure and efficient data sharing scheme for the consumer IoT applications is a huge challenge for us. The traditional cloud-based IoT has the dilemma of prolonged communication delay and privacy leakage. With the application of 5G technology, edge computing can effectively alleviate these problems. However, it can not meet the higher security requirements for the data sources' authenticity and information reliability. By combining the blockchain and smart contracts technology, this article proposes a distributed, efficient and secure data sharing scheme centered on consumer IoT devices. This architecture consists of four layers: IoT devices layer, edge storage layer, blockchain network layer and application services layer. We design smart contracts based on the attributed based access control (ABAC) and the searchable encryption algorithm, including Device Retrieval Contract (DRC), Policy Management Contract (PMC) and Authorization Verification Contract (AVC). Through the implementation of simulated experiments, we prove that our proposed architecture can satisfy the large-scale data access requests and bring a tolerable level of communication overhead.

Blockchain Enabled Data Sharing Scheme for Consumer IoT Applications

Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In this paper, we mainly study the privacy and utility of the ride-sharing system, which enables multiple riders to share one driver. To solve the privacy problem and reduce the ride-sharing detouring waste, we propose a privacy-preserving ride-sharing system named pShare. To hide users’ precise locations from the service provider, we apply a zone-based travel time estimation approach to privately compute over sensitive data while cloaking each rider’s location in a zone area. To compute the matching results along with the least-detouring route, the service provider first computes the shortest path for each eligible rider combination, then compares the additional traveling time (ATT) of all combinations, and finally selects the combination with minimum ATT. We designed a secure comparing protocol by utilizing the garbled circuit, which enables the ORH server to execute the protocol with a crypto server without privacy leakage. Moreover, we apply the data packing technique, by which multiple data can be packed as one to reduce the communication and computation overhead. Through the theoretical analysis and evaluation results, we prove that pShare is a practical ride-sharing scheme that can find out the sharing riders with minimum ATT in acceptable accuracy while protecting users’ privacy.

/pdf/pshare-privacy-preserving-ride-sharing-system-with-minimum-1k9yfy81.pdf

pShare: Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route

Background With the rapid advance in percutaneous coronary intervention (PCI) technology, patients absorb large volume of iodinated contrast media (ICM). Recent studies suggested that ICM may lead to hyperthyroidism, but the association between ICM volume and thyroid is still unclear. We sought to evaluate the long-term influence of ICM on thyroid dysfunction and disease in patients received PCI. Methods This single-center retrospective study included consecutive coronary artery disease (CAD) patients. A covariance (ANCOVA) model was performed to evaluate the change of serum TSH, FT3 and FT4 before and one-year after the PCI procedure. Restricted cubic splines and logistic regression were performed to evaluate the association between ICM volume and thyroid disease. Results 2062 patients met inclusion criteria (1381 patients in the low-volume group and 681 patients in the high-volume group). The high-volume group was 0.238 ± 0.092 pmol/L higher than the low-volume group (P = 0.010) in the serum FT4. Restricted cubic splines show that there were linear dose-response relationships for ICM volume and composite endpoint and hyperthyroidism. In all models, there were significant differences in composite endpoint between the two groups. (OR 1.75, 95% CI (1.05, 2.92), P = 0.032, OR 1.73, 95% CI (1.01-2.96), P= 0.032 and OR 1.83, 95% CI (1.09-3.06), P= 0.022, respectively). The positive results were also showed for hyperthyroidism in all models (OR 2.35, 95% CI (1.14-4.84), P = 0.021, OR 10.36, 95% CI (1.20-89.00), P = 0.033 and OR 2.35, 95% CI (1.13-4.87), P = 0.022, respectively). Conclusion The present analysis gives an overview that ICM volume is associated with an increased risk of thyroid dysfunction and thyroid disease.

/pdf/impact-of-iodinated-contrast-media-in-patients-received-3p5o9lo8.pdf

Bowen Hu

Papers

A Threshold Proxy Re-Encryption Scheme for Secure IoT Data Sharing Based on Blockchain

RGB-Fusion: Monocular 3D reconstruction with learned depth prediction

Blockchain Enabled Data Sharing Scheme for Consumer IoT Applications

pShare: Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route

Impact of Iodinated Contrast Media in Patients Received Percutaneous Coronary Intervention: Focus on Thyroid Disease