7,000 research outputs found
Implementing a protected zone in a reconfigurable processor for isolated execution of cryptographic algorithms
We design and realize a protected zone inside a reconfigurable and extensible embedded RISC processor for isolated execution of cryptographic algorithms. The protected zone is a collection of processor subsystems such as functional units optimized for high-speed execution of integer operations, a small amount of local memory, and general and special-purpose registers. We outline the principles for secure software implementation of cryptographic algorithms
in a processor equipped with the protected zone. We also demonstrate the efficiency and effectiveness of the protected zone by implementing major cryptographic algorithms, namely RSA, elliptic curve cryptography, and AES in the protected zone. In terms of time efficiency, software implementations
of these three cryptographic algorithms outperform equivalent software implementations on similar processors reported in the literature. The protected zone is designed in such a modular fashion that it can easily be integrated into any RISC processor; its area overhead is considerably moderate in the sense that
it can be used in vast majority of embedded processors. The protected zone can also provide the necessary support to implement TPM functionality within the boundary of a processor
Design of programmable hardware security modules for enhancing blockchain based security framework
Globalization of the chip design and manufacturing industry has imposed significant threats to the hardware security of integrated circuits (ICs). It has made ICs more susceptible to various hardware attacks. Blockchain provides a trustworthy and distributed platform to store immutable records related to the evidence of intellectual property (IP) creation, authentication of provenance, and confidential data storage. However, blockchain encounters major security challenges due to its decentralized nature of ledgers that contain sensitive data. The research objective is to design a dedicated programmable hardware security modules scheme to safeguard and maintain sensitive information contained in the blockchain networks in the context of the IC supply chain. Thus, the blockchain framework could rely on the proposed hardware security modules and separate the entire cryptographic operations within the system as stand-alone hardware units. This work put forth a novel approach that could be considered and utilized to enhance blockchain security in real-time. The critical cryptographic components in blockchain secure hash algorithm-256 (SHA-256) and the elliptic curve digital signature algorithm are designed as separate entities to enhance the security of the blockchain framework. Physical unclonable functions are adopted to perform authentication of transactions in the blockchain. Relative comparison of designed modules with existing works clearly depicts the upper hand of the former in terms of performance parameters
Improving Air Interface User Privacy in Mobile Telephony
Although the security properties of 3G and 4G mobile networks have
significantly improved by comparison with 2G (GSM), significant shortcomings
remain with respect to user privacy. A number of possible modifications to 2G,
3G and 4G protocols have been proposed designed to provide greater user
privacy; however, they all require significant modifications to existing
deployed infrastructures, which are almost certainly impractical to achieve in
practice. In this article we propose an approach which does not require any
changes to the existing deployed network infrastructures or mobile devices, but
offers improved user identity protection over the air interface. The proposed
scheme makes use of multiple IMSIs for an individual USIM to offer a degree of
pseudonymity for a user. The only changes required are to the operation of the
authentication centre in the home network and to the USIM, and the scheme could
be deployed immediately since it is completely transparent to the existing
mobile telephony infrastructure. We present two different approaches to the use
and management of multiple IMSIs
Enhancing an Embedded Processor Core with a Cryptographic Unit for Performance and Security
We present a set of low-cost architectural enhancements to accelerate the execution of certain arithmetic operations common in cryptographic applications on an extensible embedded processor core. The proposed enhancements are generic in the sense that they can be beneficially applied in almost any RISC processor. We implemented the enhancements in form of a cryptographic unit (CU) that offers the programmer an extended instruction set. The CU features a 128-bit wide register file and datapath, which enables it to process 128-bit words and perform 128-bit loads/stores. We analyze the speed-up factors for some arithmetic operations and public-key cryptographic algorithms obtained through
these enhancements. In addition, we evaluate the hardware overhead (i.e. silicon area) of integrating the CU into an embedded RISC processor. Our experimental results show that the proposed architectural enhancements allow for a
significant performance gain for both RSA and ECC at the expense of an acceptable increase in silicon area. We also demonstrate that the proposed enhancements facilitate the protection of cryptographic algorithms against certain types of side-channel attacks and present an AES implementation
hardened against cache-based attacks as a case study
DeepSecure: Scalable Provably-Secure Deep Learning
This paper proposes DeepSecure, a novel framework that enables scalable
execution of the state-of-the-art Deep Learning (DL) models in a
privacy-preserving setting. DeepSecure targets scenarios in which neither of
the involved parties including the cloud servers that hold the DL model
parameters or the delegating clients who own the data is willing to reveal
their information. Our framework is the first to empower accurate and scalable
DL analysis of data generated by distributed clients without sacrificing the
security to maintain efficiency. The secure DL computation in DeepSecure is
performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized
realization of various components used in DL. Our optimized implementation
achieves more than 58-fold higher throughput per sample compared with the
best-known prior solution. In addition to our optimized GC realization, we
introduce a set of novel low-overhead pre-processing techniques which further
reduce the GC overall runtime in the context of deep learning. Extensive
evaluations of various DL applications demonstrate up to two
orders-of-magnitude additional runtime improvement achieved as a result of our
pre-processing methodology. This paper also provides mechanisms to securely
delegate GC computations to a third party in constrained embedded settings
Agri-Food Traceability Management using a RFID System with Privacy Protection
In this paper an agri-food traceability system based on public key cryptography and Radio Frequency Identification (RFID) technology is proposed. In order to guarantee safety in food, an efficient tracking and tracing system is required. RFID devices allow recording all useful information for traceability directly on the commodity. The security issues are discussed and two different methods based on public cryptography are proposed and evaluated. The first algorithm uses a nested RSA based structure to improve security, while the second also provides authenticity of data. An experimental analysis demonstrated that the proposed system is well suitable on PDAs to
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
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