Good Privacy Protection Research Articles

An efficient privacy-preserving authentication scheme that mitigates TA dependency in VANETs

With the constant development of wireless communication technologies, the Vehicular Ad-hoc Networks (VANETs) will become an inevitable paradigm for intelligent transportation systems. Privacy-preserving and authentication security has been a core concern in VANETs, and pseudonym-based and group signature-based privacy-preserving authentication schemes have been widely proposed. However, most research schemes rely excessively on the Trusted Authority, and vehicle request and authentication information is almost transparent in front of TAs, while they cannot guarantee the security of vehicle request information after it is intercepted. To address these issues, this paper proposes a lightweight privacy-preserving authentication scheme that mitigates TA dependency, aiming to achieve mutual privacy protection and authentication between vehicles and TAs in VANETs by improving the oblivious transfer algorithm. The security analysis proves that this scheme has good privacy protection and attack resistance, and in addition, the performance simulation and evaluation show that this scheme spends lower computation and communication overheads while ensuring lower authentication delay and packet loss rate, which is a better overall performance compared with other schemes.

Application of Big Data Privacy Protection Based on Edge Computing in the Prediction of Martial Arts Training Movement Trajectory

With the development of science and technology, edge computing and big data privacy protection are more and more widely used in various fields. The application of big data privacy protection based on edge computing in the prediction of sport trajectories for martial arts training shows good performance and privacy protection. Edge computing can process and analyze data in real time to improve the accuracy and efficiency of sport trajectory prediction. Big data privacy protection can ensure the security of athletes' personal information and training data and prevent data leakage and misuse. However, existing related works still have some deficiencies in data processing speed, accuracy, and privacy protection. In this paper, the authors address these issues and propose an edge computing-based big data privacy protection method to improve the accuracy and security of sport trajectory prediction for martial arts training.

GLPS: A Geohash-Based Location Privacy Protection Scheme.

With the development of mobile applications, location-based services (LBSs) have been incorporated into people's daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named GLPS. On the user side, GLPS uses Geohash encoding technology to convert the user's location coordinates into a string code representing a rectangular geographic area. GLPS uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user's exact location. On the anonymous server side, the scheme takes advantage of Geohash codes' geospatial gridding capabilities and GL-Tree's effective location retrieval performance to generate a k-anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user's location. We experimentally tested the performance of GLPS and compared it with three schemes: Casper, GCasper, and DLS. The experimental results and analyses demonstrate that GLPS has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association.

An Improved Density Peak Clustering Algorithm Based on Chebyshev Inequality and Differential Privacy

This study aims to improve the quality of the clustering results of the density peak clustering (DPC) algorithm and address the privacy protection problem in the clustering analysis process. To achieve this, a DPC algorithm based on Chebyshev inequality and differential privacy (DP-CDPC) is proposed. Firstly, the distance matrix is calculated using cosine distance instead of Euclidean distance when dealing with high-dimensional datasets, and the truncation distance is automatically calculated using the dichotomy method. Secondly, to solve the difficulty in selecting suitable clustering centers in the DPC algorithm, statistical constraints are constructed from the perspective of the decision graph using Chebyshev inequality, and the selection of clustering centers is achieved by adjusting the constraint parameters. Finally, to address the privacy leakage problem in the cluster analysis, the Laplace mechanism is applied to introduce noise to the local density in the process of cluster analysis, enabling the privacy protection of the algorithm. The experimental results demonstrate that the DP-CDPC algorithm can effectively select the clustering centers, improve the quality of clustering results, and provide good privacy protection performance.

Privacy protection for 3D point cloud classification based on an optical chaotic encryption scheme.

In allusion to the privacy and security problems in 3D point cloud classification, a novel privacy protection method for 3D point cloud classification based on optical chaotic encryption scheme is proposed and implemented in this paper for the first time. The mutually coupled spin-polarized vertical-cavity surface-emitting lasers (MC-SPVCSELs) subject to double optical feedback (DOF) are studied to generate optical chaos for permutation and diffusion encryption process of 3D point cloud. The nonlinear dynamics and complexity results demonstrate that the MC-SPVCSELs with DOF have high chaotic complexity and can provide tremendously large key space. All the test-sets of ModelNet40 dataset containing 40 object categories are encrypted and decrypted by the proposed scheme, and then the classification results of 40 object categories for original, encrypted, and decrypted 3D point cloud are entirely enumerated through the PointNet++. Intriguingly, the class accuracies of the encrypted point cloud are nearly all equal to 0.0000% except for the plant class with 100.0000%, indicating the encrypted point cloud cannot be classified and identified. The decryption class accuracies are very close to the original class accuracies. Therefore, the classification results verify that the proposed privacy protection scheme is practically feasible and remarkably effective. Additionally, the encryption and decryption results show that the encrypted point cloud images are ambiguous and unrecognizable, while the decrypted point cloud images are identical to original images. Moreover, this paper improves the security analysis via analyzing 3D point cloud geometric features. Eventually, various security analysis results validate that the proposed privacy protection scheme has high security level and good privacy protection effect for 3D point cloud classification.

Differential Privacy Preservation for Location Semantics.

With the rapid development of intelligent mobile terminals and communication technologies, location-based services (LBSs) have become an essential part of users' lives. LBS providers upload and share the collected users' location data. The more commonly used methods for location privacy protection are differential privacy and its extensions. However, the semantic information about location, which is an integral part of the location data, often contains sensitive user information. Most existing research methods have failed to pay enough attention to protecting the semantic information in the location data. To remedy this problem, two different scenarios for location semantic privacy protection methods are proposed in this paper to address single-point and continuous location queries. Simulation experiments on real social location check-in datasets, and comparison of three different privacy protection mechanisms, show that our solution demonstrates good service quality and privacy protection considering location semantics.

ID-Based Ring Signature against Continual Side Channel Attack

The security of the signature scheme is destroyed because its secret information of the signature system is leaked due to the side channel attack. Ring signature has good application value, which can provide more flexibility and complete anonymity. It can be used in some systems such as anonymous authentication in ad hoc networks, electronic voting and crypto coin based on blockchain. Because of the side channel attack, the private key of the ring signature system may be exposed, which may cause insecurity. We present a ring signature system against continuous side channel attack. Because of the symmetry of the ring, the user’s identity has good privacy protection. The proposed scheme is completely secure without a random oracle model and the private key disclosure rate is close to 1/3. Through the dual system technique, the existential unforgeability and unconditional anonymity of the scheme are proved in the composite order group based on the subgroup decision assumption.

Ethical guidelines for birth defect prevention recommended by experts.

China's maternal and child health work is still facing many challenges, such as unbalanced development and inadequate services. Since the adjustment of the family planning policy, the proportion of older and more productive sub-parturient women have increased, the risk for birth defects has increased, the demand for newborn safety has further increased, and the work of maternal and child health is facing new challenges. The experts from the medicine, medical ethics, sociology, and other fields put forward the principle of ethical guidance for birth defect prevention after full discussion and continuous revision based on expert proposals, which include the principle of life dignity, love, scientific principle, fair principle, respect for autonomy principle, the principle of beneficence and the principle of good privacy protection. The guideline can serve the birth defect prevention clinical practice, be better to respect and safeguard the legitimate rights and interests of people at childbearing age, eliminate ethics cognition pitfalls of birth defect prevention, and regulate the behavior of birth defect prevention-related ethics.

Efficient and Privacy-Preserving Certificate Activation for V2X Pseudonym Certificate Revocation

Vehicle to everything (V2X) technology allows the broader development of driving safety, efficiency, and comfort. Because the vehicles can quickly send and receive frequent messages from other vehicles and nearby devices, e.g., cooperative awareness message applications on the intelligent transport system (ITS), V2X requires a good security and privacy protection system to make the messages reliable for the ITS requirements. The existing standards developed in the US and Europe use many short valid period pseudonym certificates to meet the security and privacy requirements. However, this method has difficulty ensuring that revoked pseudonym certificates are treated as revoked by any vehicles because distributing revocation information on a wireless vehicular network with intermittent and rapidly changing topology is demanding. A promising approach to solving this problem is the periodic activation of released pseudonym certificates. Initially, it releases all required pseudonym certificates for a certain period to the vehicle, and pseudonym certificates can be used only after receiving an activation code. Such activation-code-based schemes have a common problem in the inefficient use of network resources between the road-side unit (RSU) and vehicles. This paper proposes an efficient and privacy-preserving activation code distribution strategy solving the problem. By adopting the unicast distribution model of modified activation code for pseudonym certificate (ACPC), our scheme can obtain benefits of efficient activation code distribution. The proposed scheme provides small communication resource usage in the V2X network with various channel options for delivering activation codes in a privacy preserved manner.

Security and Privacy Protection of Car Networking Consistent Algorithm Based on Smart Multi-Sensor

With the rapid development of the Internet and wireless communications, smart multi-sensor networks are gradually being used in various fields, and the Internet of vehicles technology combined with smart multi-sensor network came into being. However, although the Internet of vehicles technology has greatly facilitated people’s travel in traffic, its problems in security and privacy protection have also been amplified and became a major threat to the Internet of vehicles technology. Consistency algorithms are an important solution to the trust problem and are a prominent representative of distributed algorithms. In order to solve the problem of security and privacy protection of smart multi-sensors in car networking technology, in this paper, consistent algorithm and group signature verification technology are used to study the security and privacy protection performance of the Internet of vehicles in a connected random network composed of 300 nodes through artificially synthesized simulation data. Using the method in this paper, collect and analyze the experimental data of the number of messages, verification delay, traffic load, and message loss rate of randomly generated vehicles within 200 seconds and evaluate the performance of privacy protection in the maximum consistent algorithm; the results show that the verification delay will increase with the increase of the number of messages, and the more traffic load, the higher the message loss and verification delay, and the maximum consistent algorithm has good privacy protection performance. In terms of data, the RSU-based auxiliary scheme consumes 25.59% of the communication of the KPI-based signature scheme and 25.64% of the swarm scheme.

Paraffin particle dispersion costabilized by W-doped VO2 nanoparticles and chitosan for the fabrication of functional nanocellulose film

The development of functional window films for energy savings and privacy protection has attracted considerable attention. Herein, a functional nanocellulose film was fabricated via a papermaking technique by using the naturally formed network structure of nanocellulose film to obtain privacy protection and to encapsulate paraffin particles stabilized by W-doped VO2 nanoparticles and chitosan. The paraffin particles stabilized by W-doped VO2 nanoparticles and chitosan were prepared via an emulsion approach without using surfactant. The incorporation of paraffin into the nanocellulose film not only enhances the latent heat and lowers the forward transmittance of the nanocellulose film but also improves its hydrophobicity. The presence of a small amount of W-doped VO2 nanoparticles in the paraffin particle dispersion further increases the forward transmittance and latent heat of the nanocellulose film, especially at elevated temperatures. Meanwhile, the introduction of chitosan in the paraffin particle dispersion significantly improves the toughness of the nanocellulose film. Consequently, the incorporation of paraffin particles endows the nanocellulose film with hydrophobicity and toughness as well as good privacy protection and thermal insulation for energy savings at both room and elevated temperatures, making the as-prepared nanocellulose film a promising functional window film.

A federated graph neural network framework for privacy-preserving personalization

Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have considerable privacy risks due to the privacy-sensitive nature of user data. Here, we present a federated GNN framework named FedPerGNN for both effective and privacy-preserving personalization. Through a privacy-preserving model update method, we can collaboratively train GNN models based on decentralized graphs inferred from local data. To further exploit graph information beyond local interactions, we introduce a privacy-preserving graph expansion protocol to incorporate high-order information under privacy protection. Experimental results on six datasets for personalization in different scenarios show that FedPerGNN achieves 4.0% ~ 9.6% lower errors than the state-of-the-art federated personalization methods under good privacy protection. FedPerGNN provides a promising direction to mining decentralized graph data in a privacy-preserving manner for responsible and intelligent personalization.

FedVoting: A Cross-Silo Boosting Tree Construction Method for Privacy-Preserving Long-Term Human Mobility Prediction

The prediction of human mobility can facilitate resolving many kinds of urban problems, such as reducing traffic congestion, and promote commercial activities, such as targeted advertising. However, the requisite personal GPS data face privacy issues. Related organizations can only collect limited data and they experience difficulties in sharing them. These data are in “isolated islands” and cannot collectively contribute to improving the performance of applications. Thus, the method of federated learning (FL) can be adopted, in which multiple entities collaborate to train a collective model with their raw data stored locally and, therefore, not exchanged or transferred. However, to predict long-term human mobility, the performance and practicality would be impaired if only some models were simply combined with FL, due to the irregularity and complexity of long-term mobility data. Therefore, we explored the optimized construction method based on the high-efficient gradient-boosting decision tree (GBDT) model with FL and propose the novel federated voting (FedVoting) mechanism, which aggregates the ensemble of differential privacy (DP)-protected GBDTs by the multiple training, cross-validation and voting processes to generate the optimal model and can achieve both good performance and privacy protection. The experiments show the great accuracy in long-term predictions of special event attendance and point-of-interest visits. Compared with training the model independently for each silo (organization) and state-of-art baselines, the FedVoting method achieves a significant accuracy improvement, almost comparable to the centralized training, at a negligible expense of privacy exposure.

The Maximum Number of Cells With Modulated Binary Sensors

Due to the low cost and good privacy protection of binary sensors, there are many applications of binary sensors. To enhance the spatial awareness of binary sensors, researchers utilize modulators to modulate views of sensors into visible and invisible regions so that the monitoring space is segmented into small cells identified by signatures. When a warm object moves in these cells, its location or moving trajectory can be acquired more accurately with modulators than without modulators. Accordingly, the maximum number of cells (MNC) in a deployment determines partially the maximal spatial awareness of the binary sensor system. In this paper, we provide a theoretical study of the MNC, given the number of sensors and the number of modulators. We also find the sufficient and necessary conditions to achieve the MNC so that we provide the reasons why deployment cannot obtain the MNC. These conditions can guide researchers to design the MNC deployments. Furthermore, we provide a method to calculate the number of cells when a deployment sometime cannot obtain the MNC. Our experiments provide deep insights into the influences of those conditions on the MNC and the MNC on the spatial awareness of binary sensor systems.

Differential privacy protection over deep learning: An investigation of its impacted factors

Deep learning (DL) has been widely applied to achieve promising results in many fields, but it still exists various privacy concerns and issues. Applying differential privacy (DP) to DL models is an effective way to ensure privacy-preserving training and classification. In this paper, we revisit the DP stochastic gradient descent (DP-SGD) method, which has been used by several algorithms and systems and achieved good privacy protection. However, several factors, such as the sequence of adding noise, the models used etc., may impact its performance with various degrees. We empirically show that adding noise first and clipping second will not only significantly achieve high accuracy, but also accelerate convergence. Rigorous experiments have been conducted on three different datasets to train two popular DL models, Convolutional Neural Network (CNN) and Long and Short-Term Memory (LSTM). For the CNN, the accuracy rate can be increased by 3%, 8% and 10% on average for the respective datasets, and the loss value is reduced by 18%, 14% and 22% on average. For the LSTM, the accuracy rate can be increased by 18%, 13% and 12% on average, and the loss value can be reduced by 55%, 25% and 23% on average. Meanwhile, we have compared the performance of our proposed method with a state-of-the-art SGD-based technique. The results show that under the premise of a reasonable clipping threshold, the proposed method not only has better performance, but also achieve ideal privacy protection effects. The proposed alternative can be applied to many existing privacy preserving solutions.

A Many-Objective Optimization Model of Industrial Internet of Things Based on Private Blockchain

The Industrial Internet of Things (IIoT) has developed rapidly in recent years. Private blockchains with decentralization, flexible rules, and good privacy protection can be applied in the IIoT to process the massive data and tackle the security problem. However, the scalability of blockchain places a restriction on IIoT. Accordingly, this article proposes an improved algorithm based on Two_Arch2 to improve the scalability and decentralization while reducing the latency and cost of the blockchain. By integrating the private blockchain theory to IIoT and simultaneously considering the above four objectives, a many-objective blockchain-enabled IIoT model is constructed. Then an improved Two_Arch2 algorithm is utilized to solve the model. Experimental results show that the improved algorithm can effectively optimize four indicators of the model.

A comparative study of robot BABA path and traditional surgery for thyroid cancer with hyperthyroidism

Objective To evaluate the safety and feasibility of Da Vinci robot in the treatment of thyroid cancer with hyperthyroidism via bilateral axillo-breast approach (BABA). Methods The clinical data of 21 patients with thyroid cancer complicated with hyperthyroidism who received thyroidectomy + central lymph node dissection and(or)neck lymph node dissection using the Da Vinci through axillo-bilateral-breast approach in the thyroid and breast surgery of the 960th Hospital of the PLA Joint Logistics Support Force, from February 2014 to February 2019 were analyzed retrospectively. The clinical data of 21 patients were used as observation group, including 5 males and 16 females, aged (46.03±3.08) years. At the same period, 19 patients with the thyroid cancer complicated with hyperthyroidism who underwent traditional open surgery were selected as the control group, including 5 males and 14 females, aged (47.06±2.03) years. The t test, rank sum test, and χ2 test were used to compare the operation time, postoperative hospital stay, postoperative drainage volume, postoperative pain (VAS), cosmetic outcome satisfaction, and related surgical complications between the two groups. Results All the patients of the two groups completed the operation successfully. The operation time of the observation group was longer than that of the control group [(191.12±3.17) vs (145.37±6.37) min, P 0.05). There was no significant difference in Tg level and recurrence rate between the two groups (P>0.05). Conclusion Da Vinci robotic assisted treatment for thyroid cancer with hyperthyroidism is safe and feasible, which with good cosmetic and privacy protection effects. Key words: Robotics; Surgical procedures, operative; Thyroid neoplasms; Hyperthyroidism

A Speech Privacy Protection Method Based on Sound Masking and Speech Corpus

In this paper a new speech privacy protection method based on sound masking and speech corpus is proposed. Frames in the speech corpus are selected according to secret keys and pitch of the destination speech frame, and the selected masking speech frames should have the same pitch period with the destination speech frame. Selected frames are used to mask the original speech frames and protect privacy of speech. Masked speech can be transmitted safely in communication system. By removing the effect of corresponding masking frames from the masked speech with the same corpus, original speech can be recovered. Experiment results show the proposed method gains good privacy protection effect and recovered speech quality. It is robust to speech compression by waveform coding algorithms, and helpful to systems using parametric coding algorithms for compression.

Balancing Privacy and Utility in Secondary Data Use to Inform Policy

ABSTRACT
 ObjectivesUsing existing data for research can generate new knowledge and evidence for policy with relatively little cost. Privacy concerns are paramount in such secondary usage of data collected on human subjects. Information privacy protection and data security are critical considerations in reuse and repurposing of data especially linked data, longitudinal data, and large amounts of data. Data sharing and privacy protection are both in the public interest and we need to assess the risk of “doing” (sharing) as well as the risk of “not doing” (not sharing or not protecting). 
 
 ApproachThe Alberta Centre for Child Family and Community Research (the Centre) establishes the Child Youth Data Lab that links and analyzes administrative data from multiple provincial ministries and the Child Data Centre of Alberta that repurposes research data and manages its access for reuse. The Centre partners with provincial Office of the Information Privacy Commissioner, Research Ethics Boards and leaders in the research communities and technology industry to design and develop measures to enable secondary use while safeguarding the data, and to explore and adopt best practices on data sharing processes, governance, and technologies.
 ResultsIn principle current privacy laws and regulations provide good guidance in collection, use, and disclosure of data, however there is a lack of consistency in the interpretation of these laws at the operational level with regard to secondary data use. The experiences of establishing different data sharing models at the Centre through multiple initiatives are discussed. Cross-sectoral broad partnership brings understanding and builds trusting relationships, which are crucial to establishing data sharing processes. The recognition of the significance of secondary data use to provide direction for policy and program development at the executive level provides commitment for data sharing initiatives. Strong governance structure consists multi-level ministry and multiple stakeholder involvement ensures ongoing support and engagement. The highest data security standards and anonymous solution for data linkage enables the sharing of data with good privacy protection.
 ConclusionSecondary use of data to improve system performance and contributing to scientific discovery has been broadly recognized. A balance between utility and privacy can be realized through broad partnership in building proper governance, technology, processes and policies.

Anonymization for outputs of population health and health services research conducted via an online data center.

Online data centers (ODCs) are becoming increasingly popular for making health-related data available for research. Such centers provide good privacy protection during analysis by trusted researchers, but privacy concerns may still remain if the system outputs are not sufficiently anonymized. In this article, we propose a method for anonymizing analysis outputs from ODCs for publication in academic literature. We use as a model system the Secure Unified Research Environment, an online computing system that allows researchers to access and analyze linked health-related data for approved studies in Australia. This model system suggests realistic assumptions for an ODC that, together with literature and practice reviews, inform our solution design. We propose a two-step approach to anonymizing analysis outputs from an ODC. A data preparation stage requires data custodians to apply some basic treatments to the dataset before making it available. A subsequent output anonymization stage requires researchers to use a checklist at the point of downloading analysis output. The checklist assists researchers with highlighting potential privacy concerns, then applying appropriate anonymization treatments. The checklist can be used more broadly in health care research, not just in ODCs. Ease of online publication as well as encouragement from journals to submit supplementary material are likely to increase both the volume and detail of analysis results publicly available, which in turn will increase the need for approaches such as the one suggested in this paper.