A Privacy Preserving Bayesian Optimization with High Efficiency

2018 ◽  
pp. 543-555
Author(s):  
Thanh Dai Nguyen ◽  
Sunil Gupta ◽  
Santu Rana ◽  
Svetha Venkatesh
Keyword(s):  
High Efficiency ◽  
Privacy Preserving ◽  
Bayesian Optimization

scholarly journals FTP: An Approximate Fast Privacy-Preserving Equality Test Protocol for Authentication in Internet of Things

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Youwen Zhu ◽  
Yue Zhang ◽  
Jiabin Yuan ◽  
Xianmin Wang
Keyword(s):  
Internet Of Things ◽  
High Efficiency ◽  
Privacy Preserving ◽  
Fundamental Operation ◽  
Test Protocol ◽  
Simulation Experiments ◽  
Iot Applications ◽  
Running Cost ◽  
The Cost ◽  
Equality Test

Privacy-preserving string equality test is a fundamental operation of many algorithms, including privacy-preserving authentication in Internet of Things (IoT). Existing secure equality test schemes can theoretically achieve string equality comparison and preserve the private strings. However, they suffer from heavy computation and communication cost, especially while the strings are of hundreds of bits or longer, which is not suitable for IoT applications. In this paper, we propose an approximate  Fast privacy-preserving equality  Test  Protocol (FTP), which can securely complete string equality test and achieve high running efficiency at the cost of little accuracy loss. We strictly analyze the accuracy of our proposed scheme and formally prove its security. Additionally, we leverage extensive simulation experiments to evaluate the running cost, which confirms our high efficiency; for instance, our proposed FTP can securely compare two 256-bit strings within 0.7 seconds on ordinary laptops.

scholarly journals Lightweight Privacy-Preserving Data Sharing Scheme for Internet of Medical Things

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhuo Zhao ◽  
Chingfang Hsu ◽  
Lein Harn ◽  
Qing Yang ◽  
Lulu Ke
Keyword(s):  
Access Control ◽  
Data Sharing ◽  
Medical Information ◽  
High Efficiency ◽  
Privacy Preserving ◽  
Medical Data ◽  
Medical Professionals ◽  
Sharing Scheme ◽  
Internet Of Medical Things ◽  
Similar Solutions

Internet of Medical Things (IoMT) is a kind of Internet of Things (IoT) that includes patients and medical sensors. Patients can share real-time medical data collected in IoMT with medical professionals. This enables medical professionals to provide patients with efficient medical services. Due to the high efficiency of cloud computing, patients prefer to share gathering medical information using cloud servers. However, sharing medical data on the cloud server will cause security issues, because these data involve the privacy of patients. Although recently many researchers have designed data sharing schemes in medical domain for security purpose, most of them cannot guarantee the anonymity of patients and provide access control for shared health data, and further, they are not lightweight enough for IoMT. Due to these security and efficiency issues, a novel lightweight privacy-preserving data sharing scheme is constructed in this paper for IoMT. This scheme can achieve the anonymity of patients and access control of shared medical data. At the same time, it satisfies all described security features. In addition, this scheme can achieve lightweight computations by using elliptic curve cryptography (ECC), XOR operations, and hash function. Furthermore, performance evaluation demonstrates that the proposed scheme takes less computation cost through comparison with similar solutions. Therefore, it is fairly an attractive solution for efficient and secure data sharing in IoMT.

Combination of Access Control and De-Identification for Privacy Preserving in Big Data

2016 ◽  
Vol 10 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Amine Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Big Data ◽  
Access Control ◽  
Data Privacy ◽  
Scientific Community ◽  
High Efficiency ◽  
Personal Information ◽  
Privacy Preserving ◽  
Big Data Privacy ◽  
Basic Concepts ◽  
Identification Techniques

Despite of its emergence and advantages in various domains, big data still suffers from major disadvantages. Timeless, scalability, and privacy are the main problems that hinder the advance of big data. Privacy preserving has become a wide search era within the scientific community. This paper covers the problem of privacy preserving over big data by combining both access control and data de-identification techniques in order to provide a powerful system. The aim of this system is to carry on all big data properties (volume, variety, velocity, veracity, and value) to ensure protection of users' identities. After many experiments and tests, our system shows high efficiency on detecting and hiding personal information while maintaining the utility of useful data. The remainder of this report is addressed in the presentation of some known works over a privacy preserving domain, the introduction of some basic concepts that are used to build our approach, the presentation of our system, and finally the display and discussion of the main results of our experiments.

scholarly journals Noninteractive Lightweight Privacy-Preserving Auditing on Images in Mobile Crowdsourcing Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Juan Zhang ◽  
Changsheng Wan ◽  
Chunyu Zhang ◽  
Xiaojun Guo ◽  
Yongyong Chen
Keyword(s):  
Neural Network ◽  
Privacy Protection ◽  
High Efficiency ◽  
Hamming Distance ◽  
Privacy Preserving ◽  
Mobile User ◽  
Power Devices ◽  
Mobile Crowdsourcing ◽  
Real World Applications ◽  
Computation Algorithm

To determine whether images on the crowdsourcing server meet the mobile user’s requirement, an auditing protocol is desired to check these images. However, before paying for images, the mobile user typically cannot download them for checking. Moreover, since mobiles are usually low-power devices and the crowdsourcing server has to handle a large number of mobile users, the auditing protocol should be lightweight. To address the above security and efficiency issues, we propose a novel noninteractive lightweight privacy-preserving auditing protocol on images in mobile crowdsourcing networks, called NLPAS. Since NLPAS allows the mobile user to check images on the crowdsourcing server without downloading them, the newly designed protocol can provide privacy protection for these images. At the same time, NLPAS uses the binary convolutional neural network for extracting features from images and designs a novel privacy-preserving Hamming distance computation algorithm for determining whether these images on the crowdsourcing server meet the mobile user’s requirement. Since these two techniques are both lightweight, NLPAS can audit images on the crowdsourcing server in a privacy-preserving manner while still enjoying high efficiency. Experimental results show that NLPAS is feasible for real-world applications.

An Efficient Privacy-Preserving Protocol for Computing kth Minimum Value in P2P Networks

2019 ◽  
Vol 29 (09) ◽  
pp. 2050138
Author(s):  
Yue Huang ◽  
Peng Zeng ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Decision Making ◽  
Crucial Role ◽  
High Efficiency ◽  
Privacy Preserving ◽  
Data Driven ◽  
P2p Network ◽  
P2p Networks ◽  
Forward Security ◽  
Minimum Value ◽  
Maximum Value

Statistics such as [Formula: see text]th minimum value play a crucial role in our data-driven society, for example by informing decision-making. In this paper, we propose an efficient privacy-preserving protocol that allows a group of users who do not trust each other, for example in a peer-to-peer (P2P) network, to jointly calculate the [Formula: see text]th minimum value. Specifically, in our proposed protocol each user’s data is converted to a binary bit string following a certain rule. Then, the bits at the same position are aggregated from the leftmost to the rightmost. As far as we know, this is the first published scheme to obtain [Formula: see text]th minimum value in a P2P network without affecting users’ privacy. We also remark that the proposed protocol can be easily generalized to compute other statistics, such as maximum value, minimum value, and median value, while achieving high efficiency in a privacy-preserving P2P network. We then demonstrate that the proposed protocol achieves forward security and is resilient to a range of external and internal attacks.

scholarly journals Fault-Tolerant Privacy-Preserving Data Aggregation for Smart Grid

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Huadong Liu ◽  
Tianlong Gu ◽  
Yining Liu ◽  
Jingcheng Song ◽  
Zhixin Zeng
Keyword(s):  
Fault Tolerance ◽  
Secret Sharing ◽  
Data Aggregation ◽  
Smart Grids ◽  
Data Privacy ◽  
High Efficiency ◽  
Differential Privacy ◽  
Fault Tolerant ◽  
Privacy Preserving ◽  
Data Usability

In smart grids (SG), data aggregation is widely used to strike a balance between data usability and privacy protection. The fault tolerance is an important requirement to improve the robustness of data aggregation protocols, which enables normal execution of the protocols even with failures on some entities. However, to achieve fault tolerance, most schemes either sacrifice the aggregation accuracy due to the use of differential privacy or substitution strategy or need to rely on an online trusted entity to manage all user blinding factors. In this paper, a (k,n) threshold privacy-preserving data aggregation scheme named (k,n)-PDA is proposed, which reconciles data usability and data privacy through the BGN cryptosystem and achieves fault tolerance with accurate aggregation using Shamir’s secret sharing without any online trusted entity. Besides, our scheme supports the efficient changing of users’ membership. Specifically, the dynamic secrete key is distributed to n smart meters (SMs) through the threshold secret sharing algorithm. When k or more meters participate in the aggregation, the data service center (DSC) can reconstruct the key to compute the aggregate results, and less than k SMs cannot recover the key. Thus, our solution still works functionally even if up to n−k SMs fail; also, it resists attacks from the collusion of less than k SMs. Moreover, system and performance analyses demonstrate that our scheme achieves privacy, fault tolerance, and membership dynamics with high efficiency.

scholarly journals Distributed Outsourced Privacy-Preserving Gradient Descent Methods among Multiple Parties

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zuowen Tan ◽  
Haohan Zhang ◽  
Peiyi Hu ◽  
Rui Gao
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Privacy ◽  
Gradient Descent ◽  
High Efficiency ◽  
Target Function ◽  
Privacy Preserving ◽  
Descent Methods ◽  
Gradient Descent Methods ◽  
Multiple Data

The Internet of Things (IoT) is one of the latest internet evolutions. Cloud computing is an important technique which realizes the computational demand of largely distributed IoT devices/sensors by employing various machine learning models. Gradient descent methods are widely employed to find the optimal coefficients of a machine learning model in the cloud computing. Commonly, the data are distributed among multiple data owners, whereas the target function is held by the model owner. The model owner can train its model over data owner’s data and provide predictions. However, the dataset or the target function’s confidentiality may not be kept in secret during computations. Thus, security threats and privacy risks arise. To address the data and model’s privacy mentioned above, we present two new outsourced privacy-preserving gradient descent (OPPGD) method schemes over horizontally or vertically partitioned data among multiple parties, respectively. Compared to previously proposed solutions, our methods improve in comprehensiveness in a more general scene. The data privacy and the model privacy are preserved during the whole learning and prediction procedures. In addition, the execution performance evaluation demonstrates that our schemes can help the model owner to optimize its target function and provide exact prediction with high efficiency and accuracy.

scholarly journals A privacy-preserving Internet of Things device management scheme based on blockchain

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771880875 ◽  
Author(s):  
Qingsu He ◽  
Yu Xu ◽  
Zhoubin Liu ◽  
Jinhong He ◽  
You Sun ◽  
...  
Keyword(s):  
Internet Of Things ◽  
High Efficiency ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Device Management ◽  
Fine Grained ◽  
Management Scheme ◽  
A New Technique ◽  
Access Services ◽  
Support Time

Blockchain as a new technique has attracted attentions from industry and academics for sharing data across organizations. Many blockchain-based data sharing applications, such as Internet of Things devices management, need privacy-preserving access services over encrypted data with dual capabilities. On one hand, they need to keep the sensitive data private such that others cannot trace and infer sensitive data stored in the block. On the other hand, they need to support fine-grained access control both from time and users’ attributes. However, to the best of our knowledge, no blockchain systems can support time-bound and attributes-based access with high efficiency. In this article, we propose a privacy-preserving Internet of Things devices management scheme based on blockchain, which provides efficient time-bound and attribute-based access and supports key automatic revocation. The analysis and experiments show that our scheme is quite efficient and deployable.

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