Privacy Leakages in Approximate Adders

Approximate computing has recently emerged as a promising method to meet the low power requirements of digital designs. The erroneous outputs produced in approximate computing can be partially a function of each chip's process variation. We show that, in such schemes, the erroneous outputs produced on each chip instance can reveal the identity of the chip that performed the computation, possibly jeopardizing user privacy. In this work, we perform simulation experiments on 32-bit Ripple Carry Adders, Carry Lookahead Adders, and Han-Carlson Adders running at over-scaled operating points. Our results show that identification is possible, we contrast the identifiability of each type of adder, and we quantify how success of identification varies with the extent of over-scaling and noise. Our results are the first to show that approximate digital computations may compromise privacy. Designers of future approximate computing systems should be aware of the possible privacy leakages and decide whether mitigation is warranted in their application.Comment: 2017 IEEE International Symposium on Circuits and Systems (ISCAS

SRAM PUF의 신뢰성 개선을 위한 전원 공급 기법

학위논문 (석사) -- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2021. 2. 전동석.PUF (Physically Unclonable Function)은 하드웨어 레벨의 인증 과 정에서 널리 이용되는 방법이다. 그 중에서도 SRAM PUF는 가장 잘 알 려진 PUF의 방법론이다. 그러나 예측 불가능한 동작으로 인해 발생되는 낮은 재생산성과 전원 공급 과정에서 발생하는 노이즈의 문제를 가지고 있다. 본 논문에서는 효과적으로 SRAM PUF의 재생산성을 향상시킬 수 있는 두 가지 전원 공급 기법을 제안한다. 제시한 기법들은 값이 산출되 는 영역 혹은 전원 공급원의 기울기(ramp-up 시간)를 조절함으로써 원 하지 않는 비트의 뒤집힘(flipping) 현상을 줄인다. 180nm 공정으로 제 작된 테스트 칩을 이용한 측정 결과 재생산성이 2.2배 향상되었을 뿐만 아니라 NUBs(Native Unstable Bits)는 54.87% 그리고 BER (Bit Error Rate)는 55.05% 감소한 것을 확인하였다.Physically unclonable function (PUF) is a widely used hardware-level identification method. SRAM-based PUFs are the most well-known PUF topology, but they typically suffer from low reproducibility due to non-deterministic behaviors and noise during power-up process. In this work, we propose two power-up control techniques that effectively improve reproducibility of the SRAM PUFs. The techniques reduce undesirable bit flipping during evaluation by controlling either evaluation region or power supply ramp-up speed. Measurement results from the 180 nm test chip confirm that native unstable bits (NUBs) are reduced by 54.87% and bit error rate (BER) decreases by 55.05% while reproducibility increases by 2.2×.Chapter 1 Introduction 1 1.1 PUF in Hardware Securit 1 1.2 Prior Works and Motivation 2 Chapter 2 Related works and Motivation 5 2.1 Uniqueness 7 2.2 Reproducibility 7 2.3 Hold Static Noise Margin (SNM) 8 2.4 Bit Error Rate (BER) 9 2.5 PUF Static Noise Margin Ratio (PSNMratio) 9 Chapter 3 Microarchitecture-Aware Code Generation 11 3.1 Scheme 1: Developing Fingerprint in Sub-Threshold Region 13 3.2 Scheme 2: Controlling Voltage Ramp-up Speed 17 Chapter 4 Experimental Evaluation 19 4.1 Experimental Setup 19 4.2 Evaluation Results 21 Chapter 5 Conclusion 28 Bibliography 29 Abstract in Korean 33Maste

SecuCode: Intrinsic PUF Entangled Secure Wireless Code Dissemination for Computational RFID Devices

The simplicity of deployment and perpetual operation of energy harvesting devices provides a compelling proposition for a new class of edge devices for the Internet of Things. In particular, Computational Radio Frequency Identification (CRFID) devices are an emerging class of battery-free, computational, sensing enhanced devices that harvest all of their energy for operation. Despite wireless connectivity and powering, secure wireless firmware updates remains an open challenge for CRFID devices due to: intermittent powering, limited computational capabilities, and the absence of a supervisory operating system. We present, for the first time, a secure wireless code dissemination (SecuCode) mechanism for CRFIDs by entangling a device intrinsic hardware security primitive Static Random Access Memory Physical Unclonable Function (SRAM PUF) to a firmware update protocol. The design of SecuCode: i) overcomes the resource-constrained and intermittently powered nature of the CRFID devices; ii) is fully compatible with existing communication protocols employed by CRFID devices in particular, ISO-18000-6C protocol; and ii) is built upon a standard and industry compliant firmware compilation and update method realized by extending a recent framework for firmware updates provided by Texas Instruments. We build an end-to-end SecuCode implementation and conduct extensive experiments to demonstrate standards compliance, evaluate performance and security.Comment: Accepted to the IEEE Transactions on Dependable and Secure Computin

TuRaN: True Random Number Generation Using Supply Voltage Underscaling in SRAMs

Prior works propose SRAM-based TRNGs that extract entropy from SRAM arrays. SRAM arrays are widely used in a majority of specialized or general-purpose chips that perform the computation to store data inside the chip. Thus, SRAM-based TRNGs present a low-cost alternative to dedicated hardware TRNGs. However, existing SRAM-based TRNGs suffer from 1) low TRNG throughput, 2) high energy consumption, 3) high TRNG latency, and 4) the inability to generate true random numbers continuously, which limits the application space of SRAM-based TRNGs. Our goal in this paper is to design an SRAM-based TRNG that overcomes these four key limitations and thus, extends the application space of SRAM-based TRNGs. To this end, we propose TuRaN, a new high-throughput, energy-efficient, and low-latency SRAM-based TRNG that can sustain continuous operation. TuRaN leverages the key observation that accessing SRAM cells results in random access failures when the supply voltage is reduced below the manufacturer-recommended supply voltage. TuRaN generates random numbers at high throughput by repeatedly accessing SRAM cells with reduced supply voltage and post-processing the resulting random faults using the SHA-256 hash function. To demonstrate the feasibility of TuRaN, we conduct SPICE simulations on different process nodes and analyze the potential of access failure for use as an entropy source. We verify and support our simulation results by conducting real-world experiments on two commercial off-the-shelf FPGA boards. We evaluate the quality of the random numbers generated by TuRaN using the widely-adopted NIST standard randomness tests and observe that TuRaN passes all tests. TuRaN generates true random numbers with (i) an average (maximum) throughput of 1.6Gbps (1.812Gbps), (ii) 0.11nJ/bit energy consumption, and (iii) 278.46us latency

NoisFre: Noise-Tolerant Memory Fingerprints from Commodity Devices for Security Functions

Building hardware security primitives with on-device memory fingerprints is a compelling proposition given the ubiquity of memory in electronic devices, especially for low-end Internet of Things devices for which cryptographic modules are often unavailable. However, the use of fingerprints in security functions is challenged by the small, but unpredictable variations in fingerprint reproductions from the same device due to measurement noise. Our study formulates a novel and pragmatic approach to achieve highly reliable fingerprints from device memories. We investigate the transformation of raw fingerprints into a noise-tolerant space where the generation of fingerprints is intrinsically highly reliable. We derive formal performance bounds to support practitioners to easily adopt our methods for applications. Subsequently, we demonstrate the expressive power of our formalization by using it to investigate the practicability of extracting noise-tolerant fingerprints from commodity devices. Together with extensive simulations, we have employed 119 chips from five different manufacturers for extensive experimental validations. Our results, including an end-to-end implementation demonstration with a low-cost wearable Bluetooth inertial sensor capable of on-demand and runtime key generation, show that key generators with failure rates less than $10^-6$ can be efficiently obtained with noise-tolerant fingerprints with a single fingerprint snapshot to support ease-of-enrollment.Comment: Accepted to IEEE Transactions on Dependable and Secure Computing. Yansong Gao and Yang Su contributed equally to the study and are co-first authors in alphabetical orde