The Internet of Things is the network of physical devices, vehicles, home appliances and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these objects to connect and exchange data.

Achieving Efficient and Secure Data Acquisition for Cloud supported
Internet of Things in Smart Grid

Introduction:
Cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart grid systems. The IoT front-ends are responsible for data acquisition and status supervision, while the substantial amount of data is stored and managed in the cloud server. Achieving data security and system efficiency in the data acquisition and transmission process are of great significance and challenging, because the power grid-related data is sensitive and in huge amount. In this paper, we present an efficient and secure data acquisition scheme based on CP-ABE (Ciphertext Policy Attribute Based Encryption). Data acquired from the terminals will be partitioned into blocks and encrypted with its corresponding access sub-tree in sequence, thereby the data encryption and data transmission can be processed in parallel. Furthermore, we protect the information about the access tree with threshold secret sharing method, which can preserve the data privacy and integrity from users with the unauthorized sets of attributes. The formal analysis demonstrates that the proposed scheme can fulfill the security requirements of the Cloud-supported IoT in smart grid. The numerical analysis and experimental results indicate that our scheme can effectively reduce the time cost compared with other popular approaches.

Reference IEEE paper:
“Achieving Efficient and Secure Data Acquisition for Cloud-supported Internet of Things in Smart Grid” , IEEE Internet of Things Journal, IEEE 2017.

Unique ID -SBI1007

DomainCLOUD COMPUTING

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A Distributed Publisher-Driven Secure Data Sharing Scheme for Information-Centric IoT

Introduction:

In Information-Centric Internet of Things (ICIoT), IoT data can be cached throughout a network for close data copy retrievals. Such a distributed data caching environment, however, poses a challenge to flexible authorization in the network. To address this challenge, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been identified as a promising approach. However in the existing CP-ABE scheme, publishers need to retrieve attributes from a centralized server for encrypting data, which leads to high communication overhead. To solve this problem, we incorporate CP-ABE and propose a novel Distributed Publisher-driven secure Data sharing for ICIoT (DPD-ICIoT) to enable only authorized users to retrieve IoT data from distributed cache. In DPDICIoT, newly introduced Attribute Manifest (AM) is cached in the network, through which publishers can retrieve the attributes from nearby copy holders instead of a centralized attribute server. In addition, a key chain mechanism is utilized for efficient cryptographic operations, and an Automatic Attribute Self-update Mechanism (AASM) is proposed to enable fast updates of attributes without querying centralized servers. According to the performance evaluation, DPD-ICIoT achieves lower bandwidth cost compared to the existing CPABE scheme.

Reference IEEE paper:

“A Distributed Publisher-Driven Secure Data Sharing Scheme for Information-Centric IoT”, THE IEEE IOT JOURNAL, 2017.

Unique ID – SBI1077

Domain – INTERNET OF THINGS (IoT)

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An Efficient and Fine-grained Big Data Access Control Scheme with Privacy-preserving Policy

Introduction:

How to control the access of the huge amount of big data becomes a very challenging issue, especially when big data are stored in the cloud. Ciphertext-Policy Attribute based Encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. In this paper, we propose an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, we hide the whole attribute (rather than only its values) in the access policies. To assist data decryption, we also design a novel Attribute Bloom Filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. Security analysis and performance evaluation show that our scheme can preserve the privacy from any LSSS access policy without employing much overhead.

Reference IEEE paper:

“An Efficient and Fine-grained Big Data Access Control Scheme with Privacy-preserving Policy”, IEEE Internet of Things Journal, 2017.

Unique ID – SBI1078

Domain – INTERNET OF THINGS (IoT)

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Efficient and Privacy preserving Polygons Spatial Query Framework for Location-based Services

Introduction

With the pervasiveness of mobile devices and the development of wireless communication technique, location-based services (LBS) have made our life more convenient, and the polygons spatial query, which can provide more flexible LBS, has attracted considerable interest recently. However, the flourish of polygons spatial query still faces many challenges including the query information privacy. In this paper, we present an efficient and privacy-preserving polygons spatial query framework for location-based services, called Polaris. With Polaris, the LBS provider outsources the encrypted LBS data to cloud server, and the registered user can query any polygon range to get accurate LBS results without divulging his/her query information to the LBS provider and cloud server. Specifically, an efficient special polygons spatial query algorithm (SPSQ) over ciphertext is constructed, based on an improved homomorphic encryption technology over composite order group. With SPSQ, Polaris can search outsourced encrypted LBS data in cloud server by the encrypted request, and respond the encrypted polygons spatial query results accurately. Detailed security analysis shows that the proposed Polaris can resist various known security threats. In addition, performance evaluations via implementing Polaris on smartphone and workstation with real LBS dataset demonstrate Polaris’ effectiveness in term of real environment.

Reference IEEE paper:

“Efficient and Privacy-preserving Polygons Spatial Query Framework for Location-based Services”, IEEE INTERNET OF THINGS JOURNAL, 2017.

Unique ID – SBI1079

Domain – INTERNET OF THINGS (IoT)

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Follow But No Track: Privacy Preserved Profile Publishing in Cyber-Physical Social Systems

Introduction:

Due to the close correlation with individual’s physical features and status, the adoption of Cyber-Physical Social Systems (CPSSs) has been inevitably hindered by users’ privacy concerns. Such concerns keep growing as our bile devices have more embedded sensors, while the existing countermeasures only provide incapable and limited privacy preservation for sensitive physical information. Therefore, we propose a novel privacy preservation framework for CPSSs.We formulate both the privacy concerns and user expectations in CPSSs based on real-world knowledge. We also design a corresponding data publishing mechanism for users. It regulates the publishing behaviors to hide sensitive physical profiles. Meanwhile, the published data retain comprehensive social profiles for users. Our analysis demonstrates that the mechanism achieves a local maximized performance on the aspect published data size. The experiment results towards real datasets reveals that the performance is comparable to the global optimal one.

Reference IEEE paper:

“Follow But No Track: Privacy Preserved Profile Publishing in Cyber-Physical Social Systems”, IEEE Internet of Things Journal, 2017.

Unique ID – SBI1080

Domain – INTERNET OF THINGS (IoT)

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SPFM: Scalable and Privacy Preserving Friend Matching in Mobile Cloud

Introduction:

Profile (e.g., contact list, interest, mobility) matching is more than important for fostering the wide use of mobile social networks. The social networks such as Facebook, Line or We-chat recommend the friends for the users based on users personal data such as common contact list or mobility traces. However, outsourcing users’ personal information to the cloud for friend matching will raise a serious privacy concern due to the potential risk of data abusing. In this study, we propose a novel Scalable and Privacy-preserving Friend Matching protocol, or SPFM in short, which aims to provide a scalable friend matching and recommendation solutions without revealing the users personal data to the cloud. Different from the previous works which involves multiple rounds of protocols, SPFM presents a scalable solution which can prevent honest-but-curious mobile cloud from obtaining the original data and support the friend matching of multiple users simultaneously. We give detailed feasibility and security analysis on SPFM and its accuracy and security have been well demonstrated via extensive simulations. The result show that our scheme works even better when original data is large.

SPFM: Scalable and Privacy Preserving Friend Matching in Mobile Cloud

Reference IEEE paper :

“SPFM: Scalable and Privacy-preserving Friend Matching in Mobile Cloud”, IEEE Internet of Things Journal, 2017.

Unique ID – SBI1081

DomainInternet of Things (IOT)

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