A Programmable SoC-Based Accelerator for Privacy-Enhancing Technologies and Functional Encryption

A multitude of privacy-enhancing technologies (PETs) has been presented recently to solve the privacy problems of contemporary services utilizing cloud computing. Many of them are based on additively homomorphic encryption (AHE) that allows the computation of additions on encrypted data. The main technical obstacles for adaptation of PETs in practical systems are related to performance overheads compared with current privacy-violating alternatives. In this article, we present a hardware/software (HW/SW) codesign for programmable systems-on-chip (SoCs) that is designed for accelerating applications based on the Paillier encryption. Our implementation is a microcode-based multicore architecture that is suitable for accelerating various PETs using AHE with large integer modular arithmetic. We instantiate the implementation in a Xilinx Zynq-7000 programmable SoC and provide performance evaluations in real hardware. We also investigate its efficiency in a high-end Xilinx UltraScale+ programmable SoC. We evaluate the implementation with two target use cases that have relevance in PETs: privacy-preserving computation of squared Euclidean distances over encrypted data and multi-input functional encryption (FE) for inner products. Both of them represent the first hardware acceleration results for such operations, and in particular, the latter one is among the very first published implementation results of FE on any platform.Peer reviewe

A survey of state-of-the-art methods for securing medical databases

This review article presents a survey of recent work devoted to advanced state-of-the-art methods for securing of medical databases. We concentrate on three main directions, which have received attention recently: attribute-based encryption for enabling secure access to confidential medical databases distributed among several data centers; homomorphic encryption for providing answers to confidential queries in a secure manner; and privacy-preserving data mining used to analyze data stored in medical databases for verifying hypotheses and discovering trends. Only the most recent and significant work has been included

A distributed architecture for fuzzy logic systems and its application in human activity recognition

Fuzzy Logic Systems (FLS) have the full potential in handling imprecise and uncertain data due to the inherent advantages of the Fuzzy Inference System (FIS). Traditionally, fuzzy logic systems are linked to specific hardware or software systems. The literature review reveals that dispersed and distributed architectures of FLS are in high demand due to their capability to handle the complexities of fuzzy logic computations. However, the absence of best practices and standard methodologies prevents widespread adoption. As a result, some specific requirements, such as web communications and Service-Oriented Architecture (SOA), which can be found in many modern systems, are rarely adapted for FLSs. Sharing FLSs accessibility as web services (called Fuzzy-as-a-Service alias FaaS), in which the service is developed independently from a specific client platform, allows for autonomy, openness, load balancing, efficient resource allocation and eventually cost-effective, particularly for computationally intense FLSs. The proposed novel architectural solution (FaaS) is a web-based service that distributes the main services for FLS on more than one client and servers nodes that can reach multiple users. By extending the IEEE-1855 (2016) standard in terms of system definition and data exchange, this research offers a standard solution for building FaaS as a novel method of implementing fuzzy logic systems by means of a cloud-based collecting, processing, and examining data over the web. Recent advances in standardising Fuzzy Mark-up Language (IEEE 1855-2016) and its associated software libraries (such as JFML and Simpful) have made this achievable. Two different cloud service providers and software libraries (Amazon Web Services using JFML as a java-based library and Azure Web Services using Simpful as a python-based library) are exploited to realise the FaaS on the cloud. As a case study to establish the efficacy of the proposed FaaS, Human Activity Recognition (HAR) that plays a pivotal role in monitoring the health status of the Persons Under Observation (PUO)has been taken under consideration. In order to monitor the data related to HAR and physiological data, which are imprecise and uncertain in nature, various previous researchers have developed a good number of machine learning tools. However, such monitoring systems suffer from certain limitations due to the nature and amount of data being analysed. A number of experiments are carried out in order to showcase and evaluate FaaS performance in different HAR scenarios. The first scenario has been a real-time walking/running detection. Secondly, a fall detection system via FaaS is designed based on IEEE 1855-2016 and JFML. In view, the pandemic caused due to COVID-19, the third application dealt with developing a system to determine the health status of individuals by remotely monitoring their Oxygen saturation and heartbeat rate using wearable sensors. Finally, a performance comparison between a stand-alone fuzzy system and a FaaS solution for fall detection is performed on two different cloud services, namely AWS and Azure. Research findings exhibit that while the proposed algorithm can keep the same accuracy as a stand-alone fuzzy system (90%), it can significantly improve the processing time, e.g., reducing the processing time for 10K data samples from 179 to 45 seconds (78% improvement). Towards the end of this PhD project, the new IEEE 1855 extension is taken as a proposal into the consideration of the IEEE standards committee and is currently in the process of final approval in 2023

Privacy protection for wireless medical sensor data

In recent years, wireless sensor networks have been widely used in healthcare applications, such as hospital and home patient monitoring. Wireless medical sensor networks are more vulnerable to eavesdropping, modification, impersonation and replaying attacks than the wired networks. A lot of work has been done to secure wireless medical sensor networks. The existing solutions can protect the patient data during transmission, but cannot stop the inside attack where the administrator of the patient database reveals the sensitive patient data. In this paper, we propose a practical approach to prevent the inside attack by using multiple data servers to store patient data. The main contribution of this paper is securely distributing the patient data in multiple data servers and employing the Paillier and ElGamal cryptosystems to perform statistic analysis on the patient data without compromising the patients' privacy