scholarly journals A Privacy Protection Model of Data Publication Based on Game Theory

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Li Kuang ◽  
Yujia Zhu ◽  
Shuqi Li ◽  
Xuejin Yan ◽  
Han Yan ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Stackelberg Game ◽  
Rapid Development ◽  
Optimal Number ◽  
Service Development ◽  
Lower Probability ◽  
Data Publication ◽  
Development Organization ◽  
Trade Offs ◽  
New Strategy

With the rapid development of sensor acquisition technology, more and more data are collected, analyzed, and encapsulated into application services. However, most of applications are developed by untrusted third parties. Therefore, it has become an urgent problem to protect users’ privacy in data publication. Since the attacker may identify the user based on the combination of user’s quasi-identifiers and the fewer quasi-identifier fields result in a lower probability of privacy leaks, therefore, in this paper, we aim to investigate an optimal number of quasi-identifier fields under the constraint of trade-offs between service quality and privacy protection. We first propose modelling the service development process as a cooperative game between the data owner and consumers and employing the Stackelberg game model to determine the number of quasi-identifiers that are published to the data development organization. We then propose a way to identify when the new data should be learned, as well, a way to update the parameters involved in the model, so that the new strategy on quasi-identifier fields can be delivered. The experiment first analyses the validity of our proposed model and then compares it with the traditional privacy protection approach, and the experiment shows that the data loss of our model is less than that of the traditional k-anonymity especially when strong privacy protection is applied.

scholarly journals Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jing Zhao ◽  
Shubo Liu ◽  
Xingxing Xiong ◽  
Zhaohui Cai
Keyword(s):  
Time Series ◽  
Privacy Protection ◽  
Large Scale ◽  
Differential Privacy ◽  
Time Series Data ◽  
Sliding Window ◽  
Data Publishing ◽  
Series Data ◽  
Data Publication ◽  
Autocorrelation Time

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

scholarly journals Social Welfare Analysis under Different Levels of Consumers’ Privacy Regulation

2021 ◽  
Vol 16 (7) ◽  
pp. 2943-2964
Author(s):  
Xudong Lin ◽  
Xiaoli Huang ◽  
Shuilin Liu ◽  
Yulin Li ◽  
Hanyang Luo ◽  
...  
Keyword(s):  
Social Welfare ◽  
Information Sharing ◽  
Privacy Protection ◽  
Rapid Development ◽  
Specific Information ◽  
Privacy Regulation ◽  
Privacy Preference ◽  
The Cost ◽  
The Impact ◽  
Different Levels

With the rapid development of information technology, digital platforms can collect, utilize, and share large amounts of specific information of consumers. However, these behaviors may endanger information security, thus causing privacy concerns among consumers. Considering the information sharing among firms, this paper constructs a two-period duopoly price competition Hotelling model, and gives insight into the impact of three different levels of privacy regulations on industry profit, consumer surplus, and social welfare. The results show that strong privacy protection does not necessarily make consumers better off, and weak privacy protection does not necessarily hurt consumers. Information sharing among firms will lead to strong competitive effects, which will prompt firms to lower the price for new customers, thus damaging the profits of firms, and making consumers’ surplus higher. The level of social welfare under different privacy regulations depends on consumers’ product-privacy preference, and the cost of information coordination among firms. With the cost of information coordination among firms increasing, it is only in areas where consumers have greater privacy preferences that social welfare may be optimal under the weak regulation.

scholarly journals Learning With Differential Privacy

2021 ◽  
pp. 372-395
Author(s):  
Poushali Sengupta ◽  
Sudipta Paul ◽  
Subhankar Mishra
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Intrusion Detection Systems ◽  
Sensitive Information ◽  
Detection Systems ◽  
Trade Offs ◽  
Personal Level ◽  
Encryption Decryption ◽  
Individual Trees ◽  
Prevention Methods

The leakage of data might have an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection systems are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized response technique at the time of collection of the data which promises strong privacy with better utility. Differential privacy allows one to access the forest of data by describing their pattern of groups without disclosing any individual trees. The current adaption of differential privacy by leading tech companies and academia encourages authors to explore the topic in detail. The different aspects of differential privacy, its application in privacy protection and leakage of information, a comparative discussion on the current research approaches in this field, its utility in the real world as well as the trade-offs will be discussed.

scholarly journals Configuring Green Infrastructure for Urban Runoff and Pollutant Reduction Using an Optimal Number of Units

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1528 ◽  
Author(s):  
Carlos Martínez ◽  
Arlex Sanchez ◽  
Roberto Galindo ◽  
Aelaf Mulugeta ◽  
Zoran Vojinovic ◽  
...  
Keyword(s):  
Green Infrastructure ◽  
Urban Runoff ◽  
Optimal Number ◽  
Volume Reduction ◽  
Pollution Load ◽  
Flood Volume ◽  
Runoff Reduction ◽  
Trade Offs ◽  
Load Peak ◽  
Peak Runoff

Green infrastructure (GI) has been regarded as an effective intervention for urban runoff reduction. Despite the growing interest in GI, the technical knowledge that is needed to demonstrate their advantages, cost, and performance in reducing runoff and pollutants is still under research. The present paper describes a framework that aims to obtain the optimal configuration of GI (i.e., the optimal number of units distributed within the catchment) for urban runoff reduction. The research includes an assessment of the performance of GI measures dealing with pollution load, peak runoff, and flood volume reduction. The methodological framework developed includes: (1) data input, (2) GI selection and placement, (3) hydraulic and water quality modelling, and (4) assessing optimal GI measures. The framework was applied in a highly urbanized catchment in Cali, Colombia. The results suggest that if the type of GI measure and its number of units are taken into account within the optimisation process, it is possible to achieve optimal solutions to reduce the proposed reduction objectives with a lower investment cost. In addition, the results also indicate a pollution load, peak runoff, and flood volume reduction for different return periods of at least 33%, 28%, and 60%, respectively. This approach could assist water managers and their stakeholders to assess the trade-offs between different GI.

scholarly journals Privacy Protection Method for Vehicle Trajectory Based on VLPR Data

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Hua Chen ◽  
Chen Xiong ◽  
Jia-meng Xie ◽  
Ming Cai
Keyword(s):  
Mathematical Model ◽  
Data Acquisition ◽  
Privacy Protection ◽  
Rapid Development ◽  
Original Data ◽  
License Plate ◽  
Model Based ◽  
Protection Method ◽  
Novel Approach ◽  
Vehicle Trajectory

With the rapid development of data acquisition technology, data acquisition departments can collect increasingly more data. Various data from government agencies are gradually becoming available to the public, including license plate recognition (VLPR) data. As a result, privacy protection is becoming increasingly significant. In this paper, an adversary model based on passing time, color, type, and brand of VLPR data is proposed. Through experimental analysis, the tracking probability of a vehicle’s trajectory can be more than 94% if utilizing the original data. To decrease the tracking probability, a novel approach called the (m, n)-bucket model based on time series is proposed since previous works, such as those using generalization and bucketization models, cannot deal with data with multiple sensitive attributes (SAs) or data with time correlations. Meanwhile, a mathematical model is established to expound the privacy protection principle of the (m, n)-bucket model. By comparing the average calculated linking probability of all individuals and the actual linking probability, it is shown that the mathematical model that is proposed can well expound the privacy protection principle of the (m, n)-bucket model. Extensive experiments confirm that our technique can effectively prevent trajectory privacy disclosures.

scholarly journals Privacy Protection of Social Networks Based on Classified Attribute Encryption

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Lin Zhang ◽  
Li Li ◽  
Eric Medwedeff ◽  
Haiping Huang ◽  
Xiong Fu ◽  
...  
Keyword(s):  
Social Networks ◽  
Privacy Protection ◽  
Rapid Development ◽  
Good Friend ◽  
Privacy And Security ◽  
Protection Scheme ◽  
Data Owner ◽  
System Overhead ◽  
The Cost ◽  
Cache Mechanism

With the rapid development of social networks, privacy has also attracted attention. Based on this problem, a privacy protection scheme for social networks based on classified attribute encryption (PPSSN) is proposed for the data owner and attribute management server to manage user permissions; the approach reduces data owner overhead and also avoids use of a property management server to limit access user collusion attacks. To balance the privacy and security of data publication, this scheme classifies users and designs access control for different users and different privileges. In addition, this paper also introduces a good friend data cache mechanism to improve and optimize the original scheme to reduce the cost of decryption. The efficiency and system overhead of the proposed scheme are compared and analyzed based on experiments. The experiments show that the proposed scheme improves query efficiency, reduces system cost, and enhances privacy security.

scholarly journals A Complete MCDM Model for NPD Performance Assessment in an LED-Based Lighting Plant Factory

2018 ◽  
Vol 2018 ◽  
pp. 1-24
Author(s):  
Wen-Chin Chen ◽  
Yen-Fu Lin ◽  
Kai-Ping Liu ◽  
Hui-Pin Chang ◽  
Li-Yi Wang ◽  
...  
Keyword(s):  
Decision Making ◽  
Product Development ◽  
New Product Development ◽  
Crop Yields ◽  
Rapid Development ◽  
New Product ◽  
Fuzzy Decision Making ◽  
Trade Offs ◽  
Fuzzy Delphi ◽  
Plant Factory

Globally, industries and economies have undergone rapid development and expansion over the last several decades. As a result, global warming and environmental contaminations have resulted in climate change and jeopardized food security. In many developing countries, already decreasing crop yields are threatened by extreme weather and soil damaged by genetically modified food, making environmental problems worse and increasing food and organic product prices. For these reasons, this study proposes a hybrid multicriteria decision-making (MCDM) model for new product development (NPD) in the light-emitting diode- (LED-) based lighting plant factory. First, literature reviews and expert interviews are employed in constructing a list of decision-making objectives and criteria for new product development. Then, a fuzzy Delphi method (FDM) is used to screen the elements of the objectives and criteria, while a fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is used to determine the relationships among the objectives and criteria. Finally, a fuzzy analytic network process (FANP) and a composite priority vector (CPV) are manipulated to determine the relative importance weights of the critical objectives and criteria. Results show that the proposed method can create a useful and assessable MCDM model for decision-making applications in new product development, and a case study is herein performed to validate the feasibility of the proposed model in a Taiwanese LED-based lighting plant factory, which not only provides the decision-makers with a feasible hierarchical data structure for decision-making guidance but also increases the competitive advantages of trade-offs on developing novel products.

scholarly journals Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2307 ◽  
Author(s):  
Yancheng Shi ◽  
Zhenjiang Zhang ◽  
Han-Chieh Chao ◽  
Bo Shen
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Large Scale ◽  
Data Centers ◽  
Personal Information ◽  
Rapid Development ◽  
Data Availability ◽  
Personal Privacy ◽  
Data Anonymization ◽  
Data Privacy Protection

With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the data. Therefore, how to make the best use of the data in the risk of personal information leakage has become a popular research topic. The most common method of data privacy protection is the data anonymization, which has two main problems: (1) The availability of information after clustering will be reduced, and it cannot be flexibly adjusted. (2) Most methods are static. When the data is released multiple times, it will cause personal privacy leakage. To solve the problems, this article has two contributions. The first one is to propose a new method based on micro-aggregation to complete the process of clustering. In this way, the data availability and the privacy protection can be adjusted flexibly by considering the concepts of distance and information entropy. The second contribution of this article is to propose a dynamic update mechanism that guarantees that the individual privacy is not compromised after the data has been subjected to multiple releases, and minimizes the loss of information. At the end of the article, the algorithm is simulated with real data sets. The availability and advantages of the method are demonstrated by calculating the time, the average information loss and the number of forged data.

scholarly journals DataCite: Lessons Learned on Persistent Identifiers for Research Data

2017 ◽  
Vol 11 (2) ◽  
pp. 39-47 ◽  
Author(s):  
Laura Rueda ◽  
Martin Fenner ◽  
Patricia Cruse
Keyword(s):  
Collaborative Research ◽  
Research Data ◽  
Lessons Learned ◽  
Data Reuse ◽  
Service Development ◽  
Key Factors ◽  
Data Publication ◽  
Data Citation ◽  
Share Information ◽  
Insight Into

Data are the infrastructure of science and they serve as the groundwork for scientific pursuits. Data publication has emerged as a game-changing breakthrough in scholarly communication. Data form the outputs of research but also are a gateway to new hypotheses, enabling new scientific insights and driving innovation. And yet stakeholders across the scholarly ecosystem, including practitioners, institutions, and funders of scientific research are increasingly concerned about the lack of sharing and reuse of research data. Across disciplines and countries, researchers, funders, and publishers are pushing for a more effective research environment, minimizing the duplication of work and maximizing the interaction between researchers. Availability, discoverability, and reproducibility of research outputs are key factors to support data reuse and make possible this new environment of highly collaborative research. An interoperable e-infrastructure is imperative in order to develop new platforms and services for to data publication and reuse. DataCite has been working to establish and promote methods to locate, identify and share information about research data. Along with service development, DataCite supports and advocates for the standards behind persistent identifiers (in particular DOIs, Digital Object Identifiers) for data and other research outputs. Persistent identifiers allow different platforms to exchange information consistently and unambiguously and provide a reliable way to track citations and reuse. Because of this, data publication can become a reality from a technical standpoint, but the adoption of data publication and data citation as a practice by researchers is still in its early stages. Since 2009, DataCite has been developing a series of tools and services to foster the adoption of data publication and citation among the research community. Through the years, DataCite has worked in a close collaboration with interdisciplinary partners on these issues and we have gained insight into the development of data publication workflows. This paper describes the types of different actions and the lessons learned by DataCite. 

scholarly journals Phytonematodes as a limiting biotic factor of agricultural production in the world (review)

2021 ◽  
Author(s):  
K. A. Kalatur ◽  
L. A. Yanse
Keyword(s):  
Rapid Development ◽  
Molecular Genetic ◽  
Nematode Species ◽  
Bursaphelenchus Xylophilus ◽  
Biotic Factor ◽  
Xiphinema Index ◽  
New Strategy ◽  
Aphelenchoides Besseyi ◽  
Environmentally Safe ◽  
Meloidogyne Spp

Purpose. To analyze domestic and foreign scientific literature on the species composition and harmfulness of the world's most dangerous parasitic species of phytonematodes in crops. Results. Today, according to the available literature, the most dangerous species of phytonematodes include: gall nematode (Meloidogyne spp.), cyst-forming nematode (Heterodera spp. and Globodera spp.), root lesion nematode (Pratylenchus spp.), banana drill nematode (Radoholus similis), stem nematode (Ditylenchus dipsaci), pine stem nematode (Bursaphelenchus xylophilus), reniform nematode (Rotylenchulus reniformis), xiphinema index (Xiphinema index), false head nematode (Nacobbus aberrans), and rice leaf nematode (Aphelenchoides besseyi). Conclusions. The results of research on the prevalence and harmfulness of parasitic nematode species in crops convince us of the need for a more detailed study of this group of microorganisms. Due to the rapid development of molecular genetic methods in the last decade, scientists have been able to expand and improve their knowledge of identifying species, races and pathotypes of phytonematodes, their biological and environmental characteristics, and most importantly, to discover and understand extremely complex mechanisms of parasite and host plants. Nematologists are confident that further research in these and other areas in the future will create a basis for developing a new strategy for long-term and environmentally safe control of these dangerous plant parasites.

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