scholarly journals Privacy-Preserving Attribute-Based Keyword Search with Traceability and Revocation for Cloud-Assisted IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Kai Zhang ◽  
Yanping Li ◽  
Laifeng Lu
Keyword(s):  
Keyword Search ◽  
Rapid Development ◽  
Security Analysis ◽  
Privacy Preserving ◽  
User Privacy ◽  
Fine Grained ◽  
Important Challenge ◽  
Efficient Construction ◽  
Access Policies ◽  
Search Control

With the rapid development of cloud computing and Internet of Things (IoT) technology, it is becoming increasingly popular for source-limited devices to outsource the massive IoT data to the cloud. How to protect data security and user privacy is an important challenge in the cloud-assisted IoT environment. Attribute-based keyword search (ABKS) has been regarded as a promising solution to ensure data confidentiality and fine-grained search control for cloud-assisted IoT. However, due to the fact that multiple users may have the same retrieval permission in ABKS, malicious users may sell their private keys on the Internet without fear of being caught. In addition, most of existing ABKS schemes do not protect the access policy which may contain privacy information. Towards this end, we present a privacy-preserving ABKS that simultaneously supports policy hiding, malicious user traceability, and revocation. Formal security analysis shows that our scheme can not only guarantee the confidentiality of keywords and access policies but also realize the traceability of malicious users. Furthermore, we provide another more efficient construction for public tracing.

An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 278
Author(s):  
Yongwen Du ◽  
Gang Cai ◽  
Xuejun Zhang ◽  
Ting Liu ◽  
Jinghua Jiang
Keyword(s):  
Internet Of Things ◽  
Location Privacy ◽  
Side Information ◽  
Rapid Development ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Location Based Services ◽  
User Privacy ◽  
Computational Costs ◽  
Smart Mobile

With the rapid development of GPS-equipped smart mobile devices and mobile computing, location-based services (LBS) are increasing in popularity in the Internet of Things (IoT). Although LBS provide enormous benefits to users, they inevitably introduce some significant privacy concerns. To protect user privacy, a variety of location privacy-preserving schemes have been recently proposed. Among these schemes, the dummy-based location privacy-preserving (DLP) scheme is a widely used approach to achieve location privacy for mobile users. However, the computation cost of the existing dummy-based location privacy-preserving schemes is too high to meet the practical requirements of resource-constrained IoT devices. Moreover, the DLP scheme is inadequate to resist against an adversary with side information. Thus, how to effectively select a dummy location is still a challenge. In this paper, we propose a novel lightweight dummy-based location privacy-preserving scheme, named the enhanced dummy-based location privacy-preserving(Enhanced-DLP) to address this challenge by considering both computational costs and side information. Specifically, the Enhanced-DLP adopts an improved greedy scheme to efficiently select dummy locations to form a k-anonymous set. A thorough security analysis demonstrated that our proposed Enhanced-DLP can protect user privacy against attacks. We performed a series of experiments to verify the effectiveness of our Enhanced-DLP. Compared with the existing scheme, the Enhanced-DLP can obtain lower computational costs for the selection of a dummy location and it can resist side information attacks. The experimental results illustrate that the Enhanced-DLP scheme can effectively be applied to protect the user’s location privacy in IoT applications and services.

scholarly journals An Efficient and Privacy-Preserving Multiuser Cloud-Based LBS Query Scheme

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Lu Ou ◽  
Hui Yin ◽  
Zheng Qin ◽  
Sheng Xiao ◽  
Guangyi Yang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
User Privacy ◽  
User Revocation ◽  
And Performance ◽  
Cloud Infrastructures

Location-based services (LBSs) are increasingly popular in today’s society. People reveal their location information to LBS providers to obtain personalized services such as map directions, restaurant recommendations, and taxi reservations. Usually, LBS providers offer user privacy protection statement to assure users that their private location information would not be given away. However, many LBSs run on third-party cloud infrastructures. It is challenging to guarantee user location privacy against curious cloud operators while still permitting users to query their own location information data. In this paper, we propose an efficient privacy-preserving cloud-based LBS query scheme for the multiuser setting. We encrypt LBS data and LBS queries with a hybrid encryption mechanism, which can efficiently implement privacy-preserving search over encrypted LBS data and is very suitable for the multiuser setting with secure and effective user enrollment and user revocation. This paper contains security analysis and performance experiments to demonstrate the privacy-preserving properties and efficiency of our proposed scheme.

scholarly journals Privacy-Preserving and Efficient Public Key Encryption with Keyword Search Based on CP-ABE in Cloud

Cryptography ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 28
Author(s):  
Yunhong Zhou ◽  
Shihui Zheng ◽  
Licheng Wang
Keyword(s):  
Access Control ◽  
Data Privacy ◽  
Keyword Search ◽  
Good Method ◽  
Privacy Preserving ◽  
Public Key ◽  
Public Key Encryption ◽  
Encrypted Data ◽  
Fine Grained ◽  
Attribute Based Encryption

In the area of searchable encryption, public key encryption with keyword search (PEKS) has been a critically important and promising technique which provides secure search over encrypted data in cloud computing. PEKS can protect user data privacy without affecting the usage of the data stored in the untrusted cloud server environment. However, most of the existing PEKS schemes concentrate on data users’ rich search functionalities, regardless of their search permission. Attribute-based encryption technology is a good method to solve the security issues, which provides fine-grained access control to the encrypted data. In this paper, we propose a privacy-preserving and efficient public key encryption with keyword search scheme by using the ciphertext-policy attribute-based encryption (CP-ABE) technique to support both fine-grained access control and keyword search over encrypted data simultaneously. We formalize the security definition, and prove that our scheme achieves selective indistinguishability security against an adaptive chosen keyword attack. Finally, we present the performance analysis in terms of theoretical analysis and experimental analysis, and demonstrate the efficiency of our scheme.

An Exact and Efficient Privacy-Preserving Spatiotemporal Matching in Mobile Social Networks

2016 ◽  
Vol 12 (2) ◽  
pp. 36-47 ◽  
Author(s):  
Xiuguang Li ◽  
Yuanyuan He ◽  
Ben Niu ◽  
Kai Yang ◽  
Hui Li
Keyword(s):  
Social Networks ◽  
Rapid Development ◽  
Security Analysis ◽  
Information Leakage ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Mobile Social Networks ◽  
Trust Relationships ◽  
Matching Schemes ◽  
Open Question

With the rapid development of mobile smartphone and its built-in location-aware devices, people are possible to establish trust relationships and further interaction with each other based their matched interests, hobbies, experiences, or spatiotemporal profiles. However, the possibility of sensitive information leakage and heavy computation overhead constrain the widespread use of the matching schemes in mobile social networks. Many privacy-preserving matching schemes were proposed recently years, but how to achieve privacy-preserving spatiotemporal matching exactly and efficiently remains an open question. In this paper, the authors propose a novel spatiotemporal matching scheme. The overlapping grid system is introduced into the scheme to improve the accuracy of spatiotemporal matching, and many repetitive records in a user's spatiotemporal profile are counted as one item so as to cut down the computation overhead. Their scheme decreases the spatiotemporal matching error, and promotes the efficiency of private matchmaking simultaneously. Thorough security analysis and evaluation results indicate that our scheme is effective and efficient.

scholarly journals Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation

2021 ◽  
Vol 3 (3) ◽  
pp. 250-262
Author(s):  
Jennifer S. Raj
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Privacy Preservation ◽  
Keyword Search ◽  
Power Saving ◽  
Privacy Preserving ◽  
Mobile Social Networks ◽  
User Privacy ◽  
Attribute Based Encryption ◽  
Ciphertext Policy

Several subscribing and content sharing services are largely personalized with the growing use of mobile social media technology. The end user privacy in terms of social relationships, interests and identities as well as shared content confidentiality are some of the privacy concerns in such services. The content is provided with fine-grained access control with the help of attribute-based encryption (ABE) in existing work. Decryption of privacy preserving content suffers high consumption of energy and data leakage to unauthorized people is faced when mobile social networks share privacy preserving data. In the mobile social networks, a secure proxy decryption model with enhanced publishing and subscribing scheme is presented in this paper as a solution to the aforementioned issues. The user credentials and data confidentiality are protected by access control techniques that work on privacy preserving in a self-contained manner. Keyword search based public-key encryption with ciphertext policy attribute-based encryption is used in this model. At the end users, ciphertext decryption is performed to reduce the energy consumption by the secure proxy decryption scheme. The effectiveness and efficiency of the privacy preservation model is observed from the experimental results.

scholarly journals Fine-Grained Attribute-Based Multikeyword Search for Shared Multiowner in Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yunhong Zhou ◽  
Jiehui Nan ◽  
Licheng Wang
Keyword(s):  
Internet Of Things ◽  
Keyword Search ◽  
Security Analysis ◽  
Security And Privacy ◽  
Management Service ◽  
Specific Data ◽  
Fine Grained ◽  
Secure Search ◽  
Data Owner ◽  
Iot Devices

At present, with the popularity of Internet of things (IoT), a huge number of datasets generated by IoT devices are being uploaded to the cloud storage in remote data management service, but a series of security and privacy defects also arises, where one of the best ways for preventing data disclosure is encryption. Among them, searchable encryption (SE) is considered to be a very attractive cryptographic technology, since it allows users to search records in an encrypted form and to protect user’s data on an untrusted server. For the sake of enhancing search permission, attribute-based keyword search (ABKS) is an efficient method to provide secure search queries and fine-grained access authentications over ciphertexts. However, most existing ABKS schemes concentrate on single keyword search, which usually returns redundant and irrelevant results, so it would cost some unnecessary computation and communication resources. Furthermore, existing work in the literature mostly only supports unshared multiowner where a specific data owner owns each file, which is not able to satisfy more desired expressive search. In this work, we propose a novel attribute-based multikeyword search for shared multiowner (ABMKS-SM) primitive in IoT to achieve enhanced access control for users; meanwhile, it can support multikeyword search over ciphertexts and give a formal security analysis in the adaptive against chosen keyword attack (IND-CKA) model. Finally, we have also implemented this prototype to show efficiency when compared with some previous schemes.

scholarly journals Privacy-Preserving Meter Report Protocol of Isolated Smart Grid Devices

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Zhiwei Wang ◽  
Hao Xie
Keyword(s):  
Smart Grid ◽  
Data Aggregation ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Main Concern ◽  
Signature Scheme ◽  
Personal Privacy ◽  
Smart Meters ◽  
Fine Grained ◽  
Constrained Devices

Smart grid aims to improve the reliability, efficiency, and security of the traditional grid, which allows two-way transmission and efficiency-driven response. However, a main concern of this new technique is that the fine-grained metering data may leak the personal privacy information of the customers. Thus, the data aggregation mechanism for privacy protection is required for the meter report protocol in smart grid. In this paper, we propose an efficient privacy-preserving meter report protocol for the isolated smart grid devices. Our protocol consists of an encryption scheme with additively homomorphic property and a linearly homomorphic signature scheme, where the linearly homomorphic signature scheme is suitable for privacy-preserving data aggregation. We also provide security analysis of our protocol in the context of some typical attacks in smart grid. The implementation of our protocol on the Intel Edison platform shows that our protocol is efficient enough for the physical constrained devices, like smart meters.

Privacy-preserving conjunctive keyword search on encrypted data with enhanced fine-grained access control

2019 ◽  
Vol 23 (2) ◽  
pp. 959-989 ◽  
Author(s):  
Qiang Cao ◽  
Yanping Li ◽  
Zhenqiang Wu ◽  
Yinbin Miao ◽  
Jianqing Liu
Keyword(s):  
Access Control ◽  
Keyword Search ◽  
Privacy Preserving ◽  
Encrypted Data ◽  
Fine Grained

scholarly journals IoT Cloud Security Review

2021 ◽  
Vol 54 (4) ◽  
pp. 1-36
Author(s):  
Fei Chen ◽  
Duming Luo ◽  
Tao Xiang ◽  
Ping Chen ◽  
Junfeng Fan ◽  
...  
Keyword(s):  
Case Studies ◽  
Service Providers ◽  
Risk Mitigation ◽  
Rapid Development ◽  
Security Analysis ◽  
Cloud Security ◽  
Future Research ◽  
Security Risk ◽  
Research Attention ◽  
Cloud Systems

Recent years have seen the rapid development and integration of the Internet of Things (IoT) and cloud computing. The market is providing various consumer-oriented smart IoT devices; the mainstream cloud service providers are building their software stacks to support IoT services. With this emerging trend even growing, the security of such smart IoT cloud systems has drawn much research attention in recent years. To better understand the emerging consumer-oriented smart IoT cloud systems for practical engineers and new researchers, this article presents a review of the most recent research efforts on existing, real, already deployed consumer-oriented IoT cloud applications in the past five years using typical case studies. Specifically, we first present a general model for the IoT cloud ecosystem. Then, using the model, we review and summarize recent, representative research works on emerging smart IoT cloud system security using 10 detailed case studies, with the aim that the case studies together provide insights into the insecurity of current emerging IoT cloud systems. We further present a systematic approach to conduct a security analysis for IoT cloud systems. Based on the proposed security analysis approach, we review and suggest potential security risk mitigation methods to protect IoT cloud systems. We also discuss future research challenges for the IoT cloud security area.

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