TKSE: Trustworthy keyword search over encrypted data with two-side verifiability via blockchain

AXA Research Fund Singapor

A Privacy-Preserving, Accountable and Spam-Resilient Geo-Marketplace

Mobile devices with rich features can record videos, traffic parameters or air quality readings along user trajectories. Although such data may be valuable, users are seldom rewarded for collecting them. Emerging digital marketplaces allow owners to advertise their data to interested buyers. We focus on geo-marketplaces, where buyers search data based on geo-tags. Such marketplaces present significant challenges. First, if owners upload data with revealed geo-tags, they expose themselves to serious privacy risks. Second, owners must be accountable for advertised data, and must not be allowed to subsequently alter geo-tags. Third, such a system may be vulnerable to intensive spam activities, where dishonest owners flood the system with fake advertisements. We propose a geo-marketplace that addresses all these concerns. We employ searchable encryption, digital commitments, and blockchain to protect the location privacy of owners while at the same time incorporating accountability and spam-resilience mechanisms. We implement a prototype with two alternative designs that obtain distinct trade-offs between trust assumptions and performance. Our experiments on real location data show that one can achieve the above design goals with practical performance and reasonable financial overhead.Comment: SIGSPATIAL'19, 10 page

BMSQABSE: Design of a Bioinspired Model to Improve Security & QoS Performance for Blockchain-Powered Attribute-based Searchable Encryption Applications

Attribute-based searchable encryption (ABSE) is a sub-field of security models that allow intensive searching capabilities for cloud-based shared storage applications. ABSE Models require higher computational power, which limits their application to high-performance computing devices. Moreover, ABSE uses linear secret sharing scheme (LSSS), which requires larger storage when compared with traditional encryption models. To reduce computational complexity, and optimize storage cost, various researchers have proposed use of Machine Learning Models (MLMs), that assist in identification & removal of storage & computational redundancies. But most of these models use static reconfiguration, thus cannot be applied to large-scale deployments. To overcome this limitation, a novel combination of Grey Wolf Optimization (GWO) with Particle Swarm Optimization (PSO) model to improve Security & QoS performance for Blockchain-powered Attribute-based Searchable Encryption deployments is proposed in this text. The proposed model augments ABSE parameters to reduce its complexity and improve QoS performance under different real-time user request scenarios. It intelligently selects cyclic source groups with prime order & generator values to create bilinear maps that are used for ABSE operations. The PSO Model assists in generation of initial cyclic population, and verifies its security levels, QoS levels, and deployment costs under multiple real-time cloud scenarios. Based on this initial analysis, the GWO Model continuously tunes ABSE parameters in order to achieve better QoS & security performance levels via stochastic operations. The proposed BMSQABSE model was tested under different cloud configurations, and its performance was evaluated for healthcare deployments. Based on this evaluation, it was observed that the proposed model achieved 8.3% lower delay, with 4.9% lower energy consumption, 14.5% lower storage requirements when compared with standard ABSE models. It was able to mitigate Distributed Denial of Service (DDoS), Masquerading, Finney, and Sybil attacks, which assists in deploying the proposed model for QoS-aware highly secure deployments

R-PEKS: RBAC Enabled PEKS for Secure Access of Cloud Data

In the recent past, few works have been done by combining attribute-based access control with multi-user PEKS, i.e., public key encryption with keyword search. Such attribute enabled searchable encryption is most suitable for applications where the changing of privileges is done once in a while. However, to date, no efficient and secure scheme is available in the literature that is suitable for these applications where changing privileges are done frequently. In this paper our contributions are twofold. Firstly, we propose a new PEKS scheme for string search, which, unlike the previous constructions, is free from bi-linear mapping and is efficient by 97% compared to PEKS for string search proposed by Ray et.al in TrustCom 2017. Secondly, we introduce role based access control (RBAC) to multi-user PEKS, where an arbitrary group of users can search and access the encrypted files depending upon roles. We termed this integrated scheme as R-PEKS. The efficiency of R-PEKS over the PEKS scheme is up to 90%. We provide formal security proofs for the different components of R-PEKS and validate these schemes using a commercial dataset