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1. Polymorphic Gate based IC Watermarking Techniques

   Wang, Tian ; Cui, Xiaoxin ; Yu, Dunshan ; Aramoon, Omid ; Qu, Gang ; Cui, Xiaole ( January 2018 , Proceedings of the ASP-DAC ... Asia and South Pacific Design Automation Conference)

   Polymorphic gates are reconfigurable devices whose functionality may vary in response to the change of execution environment such as temperature, supply voltage or external control signals. This feature makes them a perfect candidate for circuit watermarking. However, polymorphic gates are hard to find because they do not exhibit the traditional structure. In this paper, we report four dual-function polymorphic gates that we have discovered using an evolutionary approach. With these gates, we propose a circuit watermarking scheme that selectively replace certain regular logic gates by the polymorphic gates. Experimental results on ISCAS and MCNC benchmark circuits demonstrate that this schememore »introduce low overhead. More specifically, the average overhead in area, speed and power are 4.10%, 2.08% and 1.17% respectively when we embed 30-bit watermark sequences. These overhead increase to 6.36%, 4.75% and 2.08% respectively when 10% of the gates in the original circuits are replaced to embed watermark up to more than 300 bits.« less
2. ChaoLock: Yet Another SAT-hard Logic Locking using Chaos Computing

   https://doi.org/10.1109/ISQED51717.2021.9424321  

   Kamali, Hadi Mardani ; Azar, Kimia Zamiri ; Homayoun, Houman ; Sasan, Avesta ( April 2021 , 22nd International Symposium on Quality Electronic Design (ISQED))

   Logic locking has been widely evaluated as a proactive countermeasure against the hardware security threats within the IC supply chain. However, the introduction of the SAT attack, and many of its derivatives, has raised big concern about this form of countermeasure. In this paper, we explore the possibility of exploiting chaos computing as a new means of logic locking. We introduce the concept of chaotic logic locking, called ChaoLock, in which, by leveraging asymmetric inputs in digital chaotic Boolean gates, we define the concept of programmability (key-configurability) to the sets of underlying initial conditions and system parameters. These initial conditionsmore »and system parameters determine the operation (functionality) of each digital chaotic Boolean gate. Also, by proposing dummy inputs in chaotic Boolean gates, we show that during reverse-engineering, the dummy inputs conceal the main functionality of the chaotic Boolean gates, which make the reverse-engineering almost impossible. By performing a security analysis of ChaoLock, we show that with no restriction on conventional CMOS-based ASIC implementation and with no test/debug compromising, none of the state-of-the-art attacks on logic locking, including the SAT attack, could reformulate chaotic Boolean gates while dummy inputs are involved and their parameters are locked. Our analysis and experimental results show that with a low number of chaotic Boolean gates mixed with CMOS digital gates, ChaoLock can guarantee resiliency against the state-of-the-art attacks on logic locking at low overhead.« less
3. One polynomial approximation to produce correctly rounded results of an elementary function for multiple representations and rounding modes

   https://doi.org/10.1145/3498664  

   Lim, Jay P. ; Nagarakatte, Santosh ( January 2022 , Proceedings of the ACM on Programming Languages)

   Mainstream math libraries for floating point (FP) do not produce correctly rounded results for all inputs. In contrast, CR-LIBM and RLIBM provide correctly rounded implementations for a specific FP representation with one rounding mode. Using such libraries for a representation with a new rounding mode or with different precision will result in wrong results due to double rounding. This paper proposes a novel method to generate a single polynomial approximation that produces correctly rounded results for all inputs for multiple rounding modes and multiple precision configurations. To generate a correctly rounded library for n -bits, our key idea is tomore »generate a polynomial approximation for a representation with n +2-bits using the round-to-odd mode. We prove that the resulting polynomial approximation will produce correctly rounded results for all five rounding modes in the standard and for multiple representations with k -bits such that | E | +1 < k ≤ n , where | E | is the number of exponent bits in the representation. Similar to our prior work in the RLIBM project, we approximate the correctly rounded result when we generate the library with n +2-bits using the round-to-odd mode. We also generate polynomial approximations by structuring it as a linear programming problem but propose enhancements to polynomial generation to handle the round-to-odd mode. Our prototype is the first 32-bit float library that produces correctly rounded results with all rounding modes in the IEEE standard for all inputs with a single polynomial approximation. It also produces correctly rounded results for any FP configuration ranging from 10-bits to 32-bits while also being faster than mainstream libraries.« less
4. Deep Neural Network Based Media Noise Predictors for Use in High-Density Magnetic Recording Turbo-Detectors

   Sayyafan, Amirhossen ; Belzer, Benjamin J ; Sivakjumar, Krishnamoorthy ; Shen, Jinlu ; Chan, Kheong Sann ; James, Ashish ( July 2019 , 2019 Magnetic Recording Conference (TMRC 2019))

   Trellis based detection with pattern dependent noise prediction (PDNP) has become standard practice in the HDD industry. In a typical single-track signal processing scheme, the received samples from the read head are first filtered by a linear equalizer with a 1D partial response (PR). The linear filter output flows into a trellis-based (e.g. BCJR) detector that employs a super-trellis based on the PR mask ISI channel and a 1D pattern dependent noise prediction (1D PDNP) algorithm. The effective channel model has a media noise term which models signal dependent noise due to, e.g., magnetic grains intersected by bit boundaries. Themore »media noise can influence two or more bit readback values. The trellis detector sends soft estimates of the coded bits to a channel decoder, which outputs estimates of the user information bits. There are two problems with traditional PDNP. First, when the number of tracks Nt simultaneously processed is greater than one, e.g. in two-dimensional magnetic recording (TDMR), the number of trellis states can become impractically large; this is the state explosion problem. Second, the relatively simple autoregressive noise model and linear prediction used in PDNP is somewhat restrictive and may not accurately represent the media noise, especially at high storage densities; this is the modeling problem. To address the state explosion problem, we separate the ISI detection and media noise prediction into two separate detectors and use the turbo-principle to exchange information between them, thus avoiding use of a super-trellis. To address the modeling problem, we design and train deep neural network (DNN) based media noise predictors. As DNN models are much more general than autoregressive models, they give a more accurate model of magnetic media noise than PDNP; this more accurate modeling results in reduced detector BERs compared to PDNP.« less
5. Beyond Software Watermarking: Traitor-Tracing for Pseudorandom Functions

   https://doi.org/10.1007/978-3-030-92078-4_9  

   Goyal, Rishab ; Kim, Sam ; Waters, Brent ; Wu, David J. ( December 2021 , ASIACRYPT 2021)

   Software watermarking schemes allow a user to embed an identifier into a piece of code such that the resulting program is nearly functionally-equivalent to the original program, and yet, it is difficult to remove the identifier without destroying the functionality of the program. Such schemes are often considered for proving software ownership or for digital rights management. Existing constructions of watermarking have focused primarily on watermarking pseudorandom functions (PRFs). In this work, we revisit the definitional foundations of watermarking, and begin by highlighting a major flaw in existing security notions. Existing security notions for watermarking only require that the identifiermore »be successfully extracted from programs that preserve the exact input/output behavior of the original program. In the context of PRFs, this means that an adversary that constructs a program which computes a quarter of the output bits of the PRF or that is able to distinguish the outputs of the PRF from random are considered to be outside the threat model. However, in any application (e.g., watermarking a decryption device or an authentication token) that relies on PRF security, an adversary that manages to predict a quarter of the bits or distinguishes the PRF outputs from random would be considered to have defeated the scheme. Thus, existing watermarking schemes provide very little security guarantee against realistic adversaries. None of the existing constructions of watermarkable PRFs would be able to extract the identifier from a program that only outputs a quarter of the bits of the PRF or one that perfectly distinguishes. To address the shortcomings in existing watermarkable PRF definitions, we introduce a new primitive called a traceable PRF. Our definitions are inspired by similar definitions from public-key traitor tracing, and aim to capture a very robust set of adversaries: namely, any adversary that produces a useful distinguisher (i.e., a program that can break PRF security), can be traced to a specific identifier. We provide a general framework for constructing traceable PRFs via an intermediate primitive called private linear constrained PRFs. Finally, we show how to construct traceable PRFs from a similar set of assumptions previously used to realize software watermarking. Namely, we obtain a single-key traceable PRF from standard lattice assumptions and a fully collusion-resistant traceable PRF from indistinguishability obfuscation (together with injective one-way functions).« less