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Neural network (NN) execution on resource-constrained edge devices is increasing. Commonly, hardware accelerators are introduced in small devices to support the execution of NNs. However, an attacker can often gain physical access to edge devices. Therefore, side-channel attacks are a potential threat to obtain valuable information about the NN. In order to keep the network secret and protect it from extraction, countermeasures are required. In this article, we propose a masked hardware accelerator for feed-forward NNs that utilizes fixed-point arithmetic and is protected against side-channel analysis (SCA). We use an existing arithmetic masking scheme and improve it to prevent incorrect results. Moreover, we transfer the scheme to the hardware layer by utilizing the glitch-extended probing model and demonstrate the security of the individual modules. To exhibit the effectiveness of the masked design, we implement it on an FPGA and measure the power consumption. The results show that with two million measurements, no secret information is leaked by means of a $t$ -test. In addition, we compare our accelerator with the masked software implementation and other hardware designs. The comparison indicates that our accelerator is up to 38 times faster than software and improves the throughput by a factor of about 4.1 compared to other masked hardware accelerators.

FPGAs promise significant performance improvements for several computations in cloud applications. However, their shared use in multi-tenant scenarios makes them susceptible to attacks. Different from classical scenarios, where the attacker has hardware access to the device, in cloud scenarios the attack is possible only from remote. As a consequence remote SCA attacks gained increasing attention in the last years. These attacks exploit that attacker and victim share with the same FPGA also the same PDN so that operation-dependent voltage fluctuations caused by the victim’s IP are observable by a voltage sensor of the attacker. While previous attacks in this domain focused on cryptographic algorithms, this work provides insights in an attack on a PUF primitive, the Loop PUF. This primitive is an interesting target for the attack since it might be used, e.g., for storing or deriving secret keys on a remote FPGA. We introduce and discuss the setup of such a remote SCA using a TDC-based voltage sensor. With this sensor, we compare the performance of classical and remote SCA attacks on the Loop PUF using two different Artix-7 FPGA and demonstrate and discuss findings regarding sampling frequency and placement. This work extends and deepens the analysis from a previously published analysis at the ASHES Workshop 2022. In particular, it provides insights into the influence of repeated measurements, measurement time, and usage of multiple TDCs on the attack performance. It also discusses the applicability of the attack to further RO-based PUF primitives. Overall the results show that remote SCA attacks on PUFs in a multi-tenant FPGA scenario have to be considered as a severe attack vector in the future.

In cycling between the mammalian host and the tsetse fly vector, trypanosomes undergo major changes in energy metabolism and surface coat composition. Early procyclic (insect) forms in the tsetse fly midgut are coated by glycoproteins known as EP and GPEET procyclins. EP expression continues in late procyclic forms, whereas GPEET is down-regulated. In culture, expression of GPEET is modulated by glycerol or glucose. Here, we demonstrate that a glycerol-responsive element of 25 nucleotides within the 3′ untranslated region of GPEETmRNA also controls expression by glucose and during development in the fly. In trypanosomes, mitochondrial ATP is produced mainly by the acetate: succinate-CoA transferase/succinyl-CoA synthetase (ASCT) cycle, the citric acid cycle, and the cytochromes. Silencing of the pyruvate dehydrogenase or succinyl-CoA synthetase from the ASCT cycle by RNA interference induces reexpression of GPEET in late procyclic forms, whereas inhibition of the citric acid cycle or the cytochromes has no effect. In contrast, inhibition of the alternative oxidase, the second branch of the electron transport chain, with salicylhydroxamic acid overrides the effect of glucose or glycerol and causes a reduction in the level of GPEETmRNA. Our results reveal a new mechanism by which expression of a surface glycoprotein is controlled by the activity of mitochondrial enzymes.