scholarly journals Multidomain Fusion Data Privacy Security Framework

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Jing Yang ◽  
Lianwei Qu ◽  
Yong Wang
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Data Publishing ◽  
Security Framework ◽  
Data Set ◽  
Fusion Data ◽  
Data Statistics ◽  
Grading Model ◽  
The Relationship ◽  
The Internet Of Things

With the collaborative collection of the Internet of Things (IoT) in multidomain, the collected data contains richer background knowledge. However, this puts forward new requirements for the security of data publishing. Furthermore, traditional statistical methods ignore the attributes sensitivity and the relationship between attributes, which makes multimodal statistics among attributes in multidomain fusion data set based on sensitivity difficult. To solve the above problems, this paper proposes a multidomain fusion data privacy security framework. First, based on attributes recognition, classification, and grading model, determine the attributes sensitivity and relationship between attributes to realize the multimode data statistics. Second, combine them with the different modal histograms to build multimodal histograms. Finally, we propose a privacy protection model to ensure the security of data publishing. The experimental analysis shows that the framework can not only build multimodal histograms of different microdomain attribute sets but also effectively reduce frequency query error.

scholarly journals Uncertain Data Privacy Protection Based on K-Anonymity via Anatomy

2012 ◽  
Vol 6-7 ◽  
pp. 64-69 ◽  
Author(s):  
Xiang Min Ren ◽  
Jing Yang ◽  
Jian Pei Zhang ◽  
Zong Fu Jia
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Uncertain Data ◽  
Information Leakage ◽  
Data Publishing ◽  
Data Set ◽  
Influence Matrix ◽  
Sensitive Attribute ◽  
Influence Degree ◽  
Equivalent Class

In traditional database domain, k-anonymity is a hotspot in data publishing for privacy protection. In this paper, we study how to use k-anonymity in uncertain data set, use influence matrix of background knowledge to describe the influence degree of sensitive attribute produced by QI attributes and sensitive attribute itself, use BK(L,K)-clustering to present equivalent class with diversity, and a novel UDAK-anonymity model via anatomy is proposed for relational uncertain data. We will extend our ideas for handling how to solve privacy information leakage problem by using UDAK-anonymity algorithms in another paper.

Privacy Protection in the Internet of Things Based on Cryptography

2015 ◽  
Vol 713-715 ◽  
pp. 2462-2466
Author(s):  
Xiu Rong Li ◽  
Shuang Zheng ◽  
Ya Li Liu
Keyword(s):  
Internet Of Things ◽  
Information Hiding ◽  
Privacy Protection ◽  
Data Privacy ◽  
Location Privacy ◽  
The Internet ◽  
Privacy Threats ◽  
Location Privacy Protection ◽  
The Internet Of Things

In this paper, we describe some privacy threats in the Internet of Things and some research works on privacy protection. We present a new scheme base on cryptosystem to protect privacy in the Internet of Things. The scheme includes location privacy protection, data privacy homomorphism mechanism and information hiding technology, and secure multi-party computation on data privacy.

scholarly journals Research on improved privacy publishing algorithm based on set cover

2019 ◽  
Vol 16 (3) ◽  
pp. 705-731
Author(s):  
Haoze Lv ◽  
Zhaobin Liu ◽  
Zhonglian Hu ◽  
Lihai Nie ◽  
Weijiang Liu ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Differential Privacy ◽  
Main Idea ◽  
Data Availability ◽  
Set Cover ◽  
Data Sets ◽  
Data Set ◽  
Query Cover ◽  
Privacy Model

With the invention of big data era, data releasing is becoming a hot topic in database community. Meanwhile, data privacy also raises the attention of users. As far as the privacy protection models that have been proposed, the differential privacy model is widely utilized because of its many advantages over other models. However, for the private releasing of multi-dimensional data sets, the existing algorithms are publishing data usually with low availability. The reason is that the noise in the released data is rapidly grown as the increasing of the dimensions. In view of this issue, we propose algorithms based on regular and irregular marginal tables of frequent item sets to protect privacy and promote availability. The main idea is to reduce the dimension of the data set, and to achieve differential privacy protection with Laplace noise. First, we propose a marginal table cover algorithm based on frequent items by considering the effectiveness of query cover combination, and then obtain a regular marginal table cover set with smaller size but higher data availability. Then, a differential privacy model with irregular marginal table is proposed in the application scenario with low data availability and high cover rate. Next, we obtain the approximate optimal marginal table cover algorithm by our analysis to get the query cover set which satisfies the multi-level query policy constraint. Thus, the balance between privacy protection and data availability is achieved. Finally, extensive experiments have been done on synthetic and real databases, demonstrating that the proposed method preforms better than state-of-the-art methods in most cases.

scholarly journals Multiorder Fusion Data Privacy-Preserving Scheme for Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Mingshan Xie ◽  
Yong Bai ◽  
Mengxing Huang ◽  
Zhuhua Hu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Privacy Protection ◽  
Data Privacy ◽  
Large Scale ◽  
Privacy Preserving ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Fusion Data ◽  
Data Privacy Protection

Privacy-preserving in wireless sensor networks is one of the key problems to be solved in practical applications. It is of great significance to solve the problem of data privacy protection for large-scale applications of wireless sensor networks. The characteristics of wireless sensor networks make data privacy protection technology face serious challenges. At present, the technology of data privacy protection in wireless sensor networks has become a hot research topic, mainly for data aggregation, data query, and access control of data privacy protection. In this paper, multiorder fusion data privacy-preserving scheme (MOFDAP) is proposed. Random interference code, random decomposition of function library, and cryptographic vector are introduced for our proposed scheme. In multiple stages and multiple aspects, the difficulty of cracking and crack costs are increased. The simulation results demonstrate that, compared with the typical Slice-Mix-AggRegaTe (SMART) algorithm, the algorithm proposed in this paper has a better data privacy-preserving ability when the traffic load is not very heavy.

scholarly journals Privacy Protection in Bigdata: A Survey

2021 ◽  
Vol 12 (2) ◽  
pp. 562-568
Author(s):  
Saumya Gupta, Et. al.
Keyword(s):  
Social Networking ◽  
Privacy Protection ◽  
Data Privacy ◽  
New Technologies ◽  
Personal Information ◽  
Research Topic ◽  
The Internet ◽  
World Systems ◽  
Public Data ◽  
The Internet Of Things

Bigdata becomes a significant sector and academics research topic. Bigdata is a two-edged sword. The rising volume of information together will increase the likelihood of blundering non-public data privacy. Due to many new technologies and innovations that pervade our everyday lives, like smartphones and social networking apps, and the Internet of Things-based intelligent-world systems, the large amount of data generated in our world has exploded. During this data processing, storage, and the use of the information it can quickly cause personal information exposure and the difficulty of interpreting the information. The aim is to incorporate this range of information into one framework for big data management and to recognize problems regarding privacy. This paper begins with the introduction of bigdata, its process, protection issues, and tools which are used to solve its problems

scholarly journals A secure and efficient privacy-preserving data aggregation algorithm

2021 ◽  
Author(s):  
Hui Dou ◽  
Yuling Chen ◽  
Yixian Yang ◽  
Yangyang Long
Keyword(s):  
Data Aggregation ◽  
Privacy Protection ◽  
Data Privacy ◽  
Cluster Head ◽  
Privacy Preserving ◽  
Data Traffic ◽  
Life Data ◽  
Private Data ◽  
Save Energy ◽  
The Internet Of Things

AbstractAs a significant part of the Internet of things, wireless sensor networks (WSNs) is frequently implemented in our daily life. Data aggregation in WSNs can realize limited transmission and save energy. In the process of data aggregation, node data information is vulnerable to be eavesdropped and attacked. Therefore, it is of great significance to the research of data aggregation privacy protection in WSNs. We propose a secure and efficient privacy-preserving data aggregation algorithm (SECPDA) based on the original clustering privacy data aggregation algorithm. In this algorithm, we utilize SEP protocol to dynamically select cluster head nodes, introduce slicing idea for the private data slicing, and generate false information for interference. A comprehensive experimental evaluation is conducted to assess the data traffic and privacy protection performance. The results demonstrate that the proposed SECPDA algorithm can effectively reduce data traffic and further improve data privacy of nodes.

scholarly journals A Privacy Protection Scheme for IoT Big Data Based on Time and Frequency Limitation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lei Zhang ◽  
Yu Huo ◽  
Qiang Ge ◽  
Yuxiang Ma ◽  
Qiqi Liu ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Privacy Protection ◽  
Social Life ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
The Internet ◽  
Protection Scheme ◽  
The Internet Of Things ◽  
Frequency Limitation

Various applications of the Internet of Things assisted by deep learning such as autonomous driving and smart furniture have gradually penetrated people’s social life. These applications not only provide people with great convenience but also promote the progress and development of society. However, how to ensure that the important personal privacy information in the big data of the Internet of Things will not be leaked when it is stored and shared on the cloud is a challenging issue. The main challenges include (1) the changes in access rights caused by the flow of manufacturers or company personnel while sharing and (2) the lack of limitation on time and frequency. We propose a data privacy protection scheme based on time and decryption frequency limitation that can be applied in the Internet of Things. Legitimate users can obtain the original data, while users without a homomorphic encryption key can perform operation training on the homomorphic ciphertext. On the one hand, this scheme does not affect the training of the neural network model, on the other hand, it improves the confidentiality of data. Besides that, this scheme introduces a secure two-party agreement to improve security while generating keys. While revoking, each attribute is specified for the validity period in advance. Once the validity period expires, the attribute will be revoked. By using storage lists and setting tokens to limit the number of user accesses, it effectively solves the problem of data leakage that may be caused by multiple accesses in a long time. The theoretical analysis demonstrates that the proposed scheme can not only ensure safety but also improve efficiency.

Student Privacy and the Law in the Internet Age

2019 ◽  
Author(s):  
Leah Plunkett ◽  
Urs Gasser ◽  
Sandra Cortesi
Keyword(s):  
Learning Environments ◽  
School Environment ◽  
Privacy Protection ◽  
Data Privacy ◽  
Digital Technologies ◽  
The United States ◽  
Legal Framework ◽  
Privacy Law ◽  
Student Privacy ◽  
High Level

New types of digital technologies and new ways of using them are heavily impacting young people’s learning environments and creating intense pressure points on the “pre-digital” framework of student privacy. This chapter offers a high-level mapping of the federal legal landscape in the United States created by the “big three” federal privacy statutes—the Family Educational Rights and Privacy Act (FERPA), the Children’s Online Privacy Protection Act (COPPA), and the Protection of Pupil Rights Amendment (PPRA)—in the context of student privacy and the ongoing digital transformation of formal learning environments (“schools”). Fissures are emerging around key student privacy issues such as: what are the key data privacy risk factors as digital technologies are adopted in learning environments; which decision makers are best positioned to determine whether, when, why, and with whom students’ data should be shared outside the school environment; what types of data may be unregulated by privacy law and what additional safeguards might be required; and what role privacy law and ethics serve as we seek to bolster related values, such as equity, agency, and autonomy, to support youth and their pathways. These and similar intersections at which the current federal legal framework is ambiguous or inadequate pose challenges for key stakeholders. This chapter proposes that a “blended” governance approach, which draws from technology-based, market-based, and human-centered privacy protection and empowerment mechanisms and seeks to bolster legal safeguards that need to be strengthen in parallel, offers an essential toolkit to find creative, nimble, and effective multistakeholder solutions.

scholarly journals Application of intelligent orienteering based on Internet of things

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Donghui Zhang ◽  
Ruijie Liu
Keyword(s):  
Internet Of Things ◽  
Network Architecture ◽  
Emergency Services ◽  
Simulation Experiment ◽  
Experimental Studies ◽  
Signal Stability ◽  
Anchor Nodes ◽  
Set Up ◽  
The Relationship ◽  
The Internet Of Things

Abstract Orienteering has gradually changed from a professional sport to a civilian sport. Especially in recent years, orienteering has been widely popularized. Many colleges and universities in China have also set up this course. With the improvement of people’s living conditions, orienteering has really become a leisure sport in modern people’s life. The reduced difficulty of sports enables more people to participate, but it also exposes a series of problems. As the existing positioning technology is relatively backward, the progress in personnel tracking, emergency services, and other aspects is slow. To solve these problems, a new intelligent orienteering application system is developed based on the Internet of things. ZigBee network architecture is adopted in the system. ZigBee is the mainstream scheme in the current wireless sensor network technology, which has many advantages such as convenient carrying, low power consumption, and signal stability. Due to the complex communication environment in mobile signal, the collected information is processed by signal amplification and signal anti-interference technology. By adding anti-interference devices, video isolators and other devices, the signal is guaranteed to the maximum extent. In order to verify the actual effect of this system, through a number of experimental studies including the relationship between error and traffic radius and the relationship between coverage and the number of anchor nodes, the data shows that the scheme studied in this paper has a greater improvement in comprehensive performance than the traditional scheme, significantly improving the accuracy and coverage. Especially the coverage is close to 100% in the simulation experiment. This research has achieved good results and can be widely used in orienteering training and competition.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

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