Key Generation and Secure Coding in Communications and Private Learning

The increasingly distributed nature of many current and future technologies has introduced many challenges for devices designed for such settings. Devices operating in such environments, such as Internet-of-Things (IoT), medical devices, connected vehicles, etc., typically have limited computational power and rely on batteries to operate. Therefore, efficiency is a paramount requirement for any algorithm designed to be implemented on these devices. Furthermore, these devices typically generate and collect huge amounts of extremely sensitive and personal data, such as health-related data, behavior-related data, etc. As a result, there is a need for security and privacy protections to guard against various attacks. Additionally, since these devices are typically resource-constrained, any algorithm or protocol needs to be efficient to enable its implementation on such devices. Efficient security and privacy solutions are essential to cope with, as well as enable, high deployment rate of such devices for various sensitive applications. In this dissertation, efficient solutions for protecting the security and privacy of data generated by such devices are explored. Low-complexity protocols for generating secret keys in static environments, along with a formulation of threshold-secure coding with a shared key and corresponding coding schemes are presented. Additionally, algorithms for coded machine unlearning for regression problems are presented, as well as a new setup and algorithm for federated learning with opt-out differential privacy are presented and evaluated.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/172704/1/aldaghri_1.pd

Acute and chronic saturated fatty acid treatment as a key instigator of the TLR-mediated inflammatory response in human adipose tissue, in vitro

A post-prandial increase in saturated fatty acids (SFAs) and glucose (Glc) activates an inflammatory response, which may be prolonged following restoration of physiological SFAs and Glc levels — a finding referred to as ‘metabolic memory'. This study examined chronic and oscillating SFAs and Glc on the inflammatory signalling pathway in human adipose tissue (AT) and adipocytes (Ads) and determined whether Ads are subject to “metabolic memory.” Abdominal (Abd) subcutaneous (Sc) explants and Ads were treated with chronic low glucose (L-Glc): 5.6 mM and high glucose (H-Glc): 17.5 mM, with low (0.2 mM) and high (2 mM) SFA for 48 h. Abd Sc explants and Ads were also exposed to the aforementioned treatment regimen for 12-h periods, with alternating rest periods of 12 h in L-Glc. Chronic treatment with L-Glc and high SFAs, H-Glc and high SFAs up-regulated key factors of the nuclear factor-κB (NFκB) pathway in Abd Sc AT and Ads (TLR4, NFκB; P<.05), whilst down-regulating MyD88. Oscillating Glc and SFA concentrations increased TLR4, NFκB, IKKβ (P<.05) in explants and Ads and up-regulated MyD88 expression (P<.05). Both tumor necrosis factor α and interleukin 6 (P<.05) secretion were markedly increased in chronically treated Abd Sc explants and Ads whilst, with oscillating treatments, a sustained inflammatory effect was noted in absence of treatment. Therefore, SFAs may act as key instigators of the inflammatory response in human AT via NFκB activation, which suggests that short-term exposure of cells to uncontrolled levels of SFAs and Glc leads to a longer-term inflammatory insult within the Ad, which may have important implications for patients with obesity and Type 2 diabetes