A Security Architecture for Data Aggregation and Access Control in Smart Grids

We propose an integrated architecture for smart grids, that supports data aggregation and access control. Data can be aggregated by home area network, building area network and neighboring area network in such a way that the privacy of customers is protected. We use homomorphic encryption technique to achieve this. The consumer data that is collected is sent to the substations where it is monitored by remote terminal units (RTU). The proposed access control mechanism gives selective access to consumer data stored in data repositories and used by different smart grid users. Users can be maintenance units, utility centers, pricing estimator units or analyzing and prediction groups. We solve this problem of access control using cryptographic technique of attribute-based encryption. RTUs and users have attributes and cryptographic keys distributed by several key distribution centers (KDC). RTUs send data encrypted under a set of attributes. Users can decrypt information provided they have valid attributes. The access control scheme is distributed in nature and does not rely on a single KDC to distribute keys. Bobba \emph{et al.} \cite{BKAA09} proposed an access control scheme, which relies on a centralized KDC and is thus prone to single-point failure. The other requirement is that the KDC has to be online, during data transfer which is not required in our scheme. Our access control scheme is collusion resistant, meaning that users cannot collude and gain access to data, when they are not authorized to access. We theoretically analyze our schemes and show that the computation overheads are low enough to be carried out in smart grids. To the best of our knowledge, ours is the first work on smart grids, which integrates these two important security components (privacy preserving data aggregation and access control) and presents an overall security architecture in smart grids.Comment: 12 Pages, 3 figure

Data-centric Misbehavior Detection in VANETs

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is very important problem with wide range of implications including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. Because of this (\emph{rational behavior}), it is more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can independently decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alert with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. Instead of revoking all the secret credentials of misbehaving nodes, as done in most schemes, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes.Comment: 12 page

A comparative study of intrathecal levobupivacaine-clonidine and bupivacaine in the quality of anesthesia for patients undergoing hernioplasty

Background: Bupivacaine is most commonly used amino-amide drug for subarachnoid block in hernioplasty. Levobupivacaine has similar pharmacological activity to that of bupivacaine with minimal cardiotoxicity. Clonidine, an α2 adrenergic agonist, potentiates the action of local anesthetics when used intrathecally and enhances post-operative analgesia. Aims and Objectives: This prospective, comparative, observational study was aimed to compare the effects of 0.5% levobupivacaine with clonidine and 0.5% hyperbaric bupivacaine in patients undergoing hernioplasty for the quality of surgical anesthesia and hemodynamic changes with any significant intraoperative complications. Materials and Methods: After receiving approval from the institutional ethics committee and written informed consent, 80 male patients aged between 18 and 60 years, BMI 150 cm, and American society of anesthesiologists physical status1 and 2 posted for elective hernioplasty were enrolled into two equal groups of 40 patients, group LC and group B. Patients in group LC received 15 mg 0.5% isobaric levobupivacaine with 30 μg clonidine and patients in group B received 15 mg hyperbaric bupivacaine intrathecally. SPSS version 20 was used for analysis, and P<0.05 was considered statistically significant. Results: In group LC, onsets of both sensory and motor blocks were delayed, whereas durations of motor and sensory block with analgesia were longer. Tachycardia, hypotension, nausea, vomiting, and shivering were observed greater in numbers in group B, whereas incidence of bradycardia was more in group LC. Conclusion: Prolonged duration of sensory and motor block, prolonged analgesic effect, and hemodynamic stability without any significant adverse effects may make this combination a better alternative to hyperbaric bupivacaine for hernioplasty

Accelerated neural induction to create hiPSC-derived peripheral neuron progenitors in vitro via an intermediate neural crest stage

University of Minnesota M.S. thesis. December 2018. Major: Stem Cell Biology. Advisor: James Dutton. 1 computer file (PDF); v, 71 pages.Pluripotent stem cells (PSCs) have the potential to differentiate into any cell type of the body, including neurons of the central and peripheral nervous system. (Takahashi, K. et. al., 2006). Current protocols for differentiating human pluripotent stem cells into posterior ectoderm-derived tissues in vitro are time consuming, however, due to an initial, protracted, rate-limiting step called neuroepithelialization. This problem of protracted timing in differentiation has hindered development of neuron differentiation protocols, limiting their usefulness in biomedical applications, drug toxicity studies or cell transplantation and/or therapies. The Dutton Lab has reduced the rate-limiting step for neural induction of human induced Pluripotent Stem Cells (hiPSCs) from 7 days to 24 hours, which has enabled rapid protocol development to explore neuronal diversity. The current work published in this thesis has been built on these findings to define an efficient, systematic step-wise differentiation protocol for the production of hiPSC-derived vagal neural crest in 4 days, which can be further differentiated into peripheral neuron progenitors and Schwann cell precursors by day 7. The peripheral neurons derived from this protocol express characteristic proteins Peripherin, Brn3A, and Islet1, while the Schwann cells express Egr1 and Etv5. This vagal crest protocol is the fastest and most efficient protocol described in the literature and has been validated on a panel of three characterised iPSC lines. Peripheral neurons derived from this protocol have been further used to study neuro-hepato interactions in an in vitro co-culture system