Linear Precoding for Broadcast Channels with Confidential Messages under Transmit-Side Channel Correlation

In this paper, we analyze the performance of regularized channel inversion (RCI) precoding in multiple-input single-output (MISO) broadcast channels with confidential messages under transmit-side channel correlation. We derive a deterministic equivalent for the achievable per-user secrecy rate which is almost surely exact as the number of transmit antennas and the number of users grow to infinity in a fixed ratio, and we determine the optimal regularization parameter that maximizes the secrecy rate. Furthermore, we obtain deterministic equivalents for the secrecy rates achievable by: (i) zero forcing precoding and (ii) single user beamforming. The accuracy of our analysis is validated by simulations of finite-size systems.Comment: to appear IEEE Communications Letter

Improving Vehicular Authentication in VANET Using

In the last several years, many types of research are focusing on Vehicular Ad-hoc Networks (VANETs) field due to the lifesaving factor. VANETs are defined as a set of vehicles in the road interact with other vehicles or with the Road Side Unit (RSU) through wireless Local Area Network (WLAN) technologies. The fundamental advantages of VANETs are enhancing the road and driver's safety and improving the vehicle security against adversaries’ attacks. Security is the most difficult issue belonging to VANETs since messages are exchanged through open wireless environments. Especially in the authentication process, the vehicles need to be authenticated before accessing or sending messages through the network. Any violation of the authentication process will open the whole network for the attack. In this paper, we applied security algorithms to improve authentication in VANETs with four stages of cryptography techniques: challenge-response authentication, digital signature, timestamping, and encryption/decryption respectively. Also, we also proposed an algorithm model and framework. Finally, we implemented the challenge-response authentication technique, and then measured and evaluated the result from the implementatio

Discrete orthogonal polynomials and difference equations of several variables

The goal of this work is to characterize all second order difference operators of several variables that have discrete orthogonal polynomials as eigenfunctions. Under some mild assumptions, we give a complete solution of the problem.Comment: minor typos correcte

Liver-specific knockout of arginase-1 leads to a profound phenotype similar to inducible whole body arginase-1 deficiency

Arginase-1 (Arg1) converts arginine to urea and ornithine in the distal step of the urea cycle in liver. We previously generated a tamoxifen-inducible Arg1 deficient mouse model (Arg1-Cre) that disrupts Arg1 expression throughout the whole body and leads to lethality ≈ 2 weeks after gene disruption. Here, we evaluate if liver-selective Arg1 loss is sufficient to recapitulate the phenotype observed in global Arg1 knockout mice, as well as to gauge the effectiveness of gene delivery or hepatocyte transplantation to rescue the phenotype. Liver-selective Arg1 deletion was induced by using an adeno-associated viral (AAV)-thyroxine binding globulin (TBG) promoter-Cre recombinase vector administered to Arg1 "floxed" mice; Arg1(fl/fl) ). An AAV vector expressing an Arg1-enhanced green fluorescent protein (Arg1-eGFP) transgene was used for gene delivery, while intrasplenic injection of wild-type (WT) C57BL/6 hepatocytes after partial hepatectomy was used for cell delivery to "rescue" tamoxifen-treated Arg1-Cre mice. The results indicate that liver-selective loss of Arg1 (> 90% deficient) leads to a phenotype resembling the whole body knockout of Arg1 with lethality ≈ 3 weeks after Cre-induced gene disruption. Delivery of Arg1-eGFP AAV rescues more than half of Arg1 global knockout male mice (survival > 4 months) but a significant proportion still succumb to the enzyme deficiency even though liver expression and enzyme activity of the fusion protein reach levels observed in WT animals. Significant Arg1 enzyme activity from engrafted WT hepatocytes into knockout livers can be achieved but not sufficient for rescuing the lethal phenotype. This raises a conundrum relating to liver-specific expression of Arg1. On the one hand, loss of expression in this organ appears to be both necessary and sufficient to explain the lethal phenotype of the genetic disorder in mice. On the other hand, gene and cell-directed therapies suggest that rescue of extra-hepatic Arg1 expression may also be necessary for disease correction. Further studies are needed in order to illuminate the detailed mechanisms for pathogenesis of Arg1-deficiency

Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies

The rapid rise of IoT and Big Data has facilitated copious data driven applications to enhance our quality of life. However, the omnipresent and all-encompassing nature of the data collection can generate privacy concerns. Hence, there is a strong need to develop techniques that ensure the data serve only the intended purposes, giving users control over the information they share. To this end, this paper studies new variants of supervised and adversarial learning methods, which remove the sensitive information in the data before they are sent out for a particular application. The explored methods optimize privacy preserving feature mappings and predictive models simultaneously in an end-to-end fashion. Additionally, the models are built with an emphasis on placing little computational burden on the user side so that the data can be desensitized on device in a cheap manner. Experimental results on mobile sensing and face datasets demonstrate that our models can successfully maintain the utility performances of predictive models while causing sensitive predictions to perform poorly.Comment: 15 pages, 5 figures, submitted to IEEE Transactions on Neural Networks and Learning System