Joint Protection Scheme for Deep Neural Network Hardware Accelerators and Models

Deep neural networks (DNNs) are utilized in numerous image processing, object detection, and video analysis tasks and need to be implemented using hardware accelerators to achieve practical speed. Logic locking is one of the most popular methods for preventing chip counterfeiting. Nevertheless, existing logic-locking schemes need to sacrifice the number of input patterns leading to wrong output under incorrect keys to resist the powerful satisfiability (SAT)-attack. Furthermore, DNN model inference is fault-tolerant. Hence, using a wrong key for those SAT-resistant logic-locking schemes may not affect the accuracy of DNNs. This makes the previous SAT-resistant logic-locking scheme ineffective on protecting DNN accelerators. Besides, to prevent DNN models from being illegally used, the models need to be obfuscated by the designers before they are provided to end-users. Previous obfuscation methods either require long time to retrain the model or leak information about the model. This paper proposes a joint protection scheme for DNN hardware accelerators and models. The DNN accelerator is modified using a hardware key (Hkey) and a model key (Mkey). Different from previous logic locking, the Hkey, which is used to protect the accelerator, does not affect the output when it is wrong. As a result, the SAT attack can be effectively resisted. On the other hand, a wrong Hkey leads to substantial increase in memory accesses, inference time, and energy consumption and makes the accelerator unusable. A correct Mkey can recover the DNN model that is obfuscated by the proposed method. Compared to previous model obfuscation schemes, our proposed method avoids model retraining and does not leak model information

Algorithmic Obfuscation for LDPC Decoders

In order to protect intellectual property against untrusted foundry, many logic-locking schemes have been developed. The main idea of logic locking is to insert a key-controlled block into a circuit to make the circuit function incorrectly without right keys. However, in the case that the algorithm implemented by the circuit is naturally fault-tolerant or self-correcting, existing logic-locking schemes do not affect the system performance much even if wrong keys are used. One example is low-density parity-check (LDPC) error-correcting decoder, which has broad applications in digital communications and storage. This paper proposes two algorithmic-level obfuscation methods for LDPC decoders. By modifying the decoding process and locking the stopping criterion, our new designs substantially degrade the decoder throughput and/or error-correcting performance when the wrong key is used. Besides, our designs are also resistant to the SAT, AppSAT and removal attacks. For an example LDPC decoder, our proposed methods reduce the throughput to less than 1/3 and/or increase the decoder error rate by at least two orders of magnitude with only 0.33% area overhead

Molecular features of hepatitis E virus from farmed rabbits in Shandong province, China

[EN] This study was undertaken to investigate the genetic variability of hepatitis E virus (HEV) from farmed rabbits in Shandong province, China. A total of 50 fresh faecal samples from 5 rabbit farms were collected and subjected to reverse transcription and nested polymerase chain reaction (RT-nPCR) for a fragment sequence of HEV capsid gene. The results demonstrated that HEV RNA was observed in 6 faecal samples (6/50, 12.0%). In addition, the result of phylogenetic analysis showed that the 6 HEV isolates were classified into HEV-3 genotype with other rabbit HEV isolates from other countries, and shared 85.2-87.2%, 81.5-83.1%, and 77.0-78.6% nucleotide similarities with rabbit HEV isolates from Korea, the United States and France, respectively. To sum up, the HEV isolated in this study from farmed rabbits belongs to the HEV-3 genotype, and the zoonotic ability and pathogenesis of the rabbit HEV merit further study due to the fact that HEV-3 genotype has the potential to trigger zoonotic infections.This study was supported by the Intramural Fund of Hebei University of Economics and Business in 2018 (2018PY17) and the Development Plan of Science and Technology of Tai’an, China (2016NS1051)Zhang, H.; Zhou, Y.; Liu, J. (2018). Molecular features of hepatitis E virus from farmed rabbits in Shandong province, China. World Rabbit Science. 26(4):307-312. https://doi.org/10.4995/wrs.2018.10225SWORD307312264Ahn H.S., Park B.J., Han S.H., Kim Y.H., Kim D.H., Kim B.S., Lee J.B., Park S.Y., Song C.S., Lee S.W., Choi I.S. 2017. Prevalence and genetic features of rabbit hepatitis E virus in Korea. J. Med. Virol., 89: 1995-2002. https://doi.org/10.1002/jmv.24875Burt S.A., Veltman J., Hakze-van der Honing R., Schmitt H., Poel W.H. 2016. 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Wildlife reservoir for hepatitis E virus, Southwestern France. Emerg Infect. Dis., 21: 1224. https://doi.org/10.3201/eid2107.141909Li S., Liu M., Cong J., Zhou Y., Miao Z. 2017. Detection and characterization of Hepatitis E Virus in goats at slaughterhouse in Tai'an Region, China. Biomed. Res. Int., 3723650. https://doi.org/10.1155/2017/3723650Liu P., Bu Q.N., Wang L., Han J., Du R.J., Lei Y.X., Ouyang Y.Q., Li J., Zhu Y.H., Lu F.M. 2013. Transmission of hepatitis E virus from rabbits to cynomolgus macaques. Emerg. Infect. Dis., 19: 559-565. https://doi.org/10.3201/eid1904.120827Meng X.J. 2010a. Hepatitis E virus: animal reservoirs and zoonotic risk. Vet. Microbiol., 140: 256-265. https://doi.org/10.1016/j.vetmic.2009.03.017Meng X.J. 2010b. Novel strains of hepatitis E virus identified from humans and other animal species: is hepatitis E a zoonosis? J. Hepatol., 33: 842-845. https://doi.org/10.1016/S0168-8278(00)80319-0Meng X.J. 2011. From barnyard to food table: the omnipresence of hepatitis E virus and risk for zoonotic infection and food safety. Virus Res., 161: 23-30. https://doi.org/10.1016/j.virusres.2011.01.016Pavio N., Meng X.J., Doceul V. 2015. Zoonotic origin of hepatitis E. Curr. Opin. Virol., 10: 34-41. https://doi.org/10.1016/j.coviro.2014.12.006Pérez-Gracia M.T., Suay B., Mateos-Lindemann M.L. 2014. Hepatitis E: an emerging disease. Infect. Genet. Evol., 22: 40-59. https://doi.org/10.1016/j.meegid.2014.01.002Purcell R., Emerson S. 2008. Hepatitis E: an emerging awareness of an old disease. J. Hepatol., 48: 494-503. https://doi.org/10.1016/j.jhep.2007.12.008Purdy M.A., Khudyakov Y.E. 2010. Evolutionary history and population dynamics of hepatitis E virus. PLoS One, 5:e14376. https://doi.org/10.1371/journal.pone.0014376Ryll R., Eiden M., Heuser E., Weinhardt M., Ziege M., Höper D., Groschup M.H., Heckel G., Johne R., Ulrich R.G. 2018. 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Can Social Disconnectedness Inhibit Online Trade? Examining the Effects of Digital Distance on Peer-to-peer Lending

The extant literature has shown that offline group proximity manifests in online peer-to-peer lending platforms, inhibiting online transactions in those markets. The findings of this research suggest that digital distance, as measured by the rate of Facebook friendship between country pairs, can also influence lending actions in bi-country lending. Building on a dataset from Kiva.org, we show that digital distance significantly and negatively affects bi-country lending actions, on top of other distance-related barriers discussed in the literature. The results also shed light on the role of government policies regarding local IT infrastructure and Internet freedom, revealing that greater levels of IT infrastructure and Internet freedom can compensate for the negative effect of digital distance on prosocial lending

Semi-Lazy Learning Approach to Dynamic Spatio-Temporal Data Analysis

Ph.DDOCTOR OF PHILOSOPH