Secure Featurization and Applications to Secure Phishing Detection

Secure inference allows a server holding a machine learning (ML) inference algorithm with private weights, and a client with a private input, to obtain the output of the inference algorithm, without revealing their respective private inputs to one another. While this problem has received plenty of attention, existing systems are not applicable to a large class of ML algorithms (such as in the domain of Natural Language Processing) that perform featurization as their first step. In this work, we address this gap and make the following contributions: 1. We initiate the formal study of secure featurization and its use in conjunction with secure inference protocols. 2. We build secure featurization protocols in the one/two/three-server settings that provide a tradeoff between security and efficiency. 3. Finally, we apply our algorithms in the context of secure phishing detection and evaluate our end-to-end protocol on models that are commonly used for phishing detection

Effect of surgical experience and spine subspecialty on the reliability of the {AO} Spine Upper Cervical Injury Classification System

OBJECTIVE The objective of this paper was to determine the interobserver reliability and intraobserver reproducibility of the AO Spine Upper Cervical Injury Classification System based on surgeon experience (< 5 years, 5–10 years, 10–20 years, and > 20 years) and surgical subspecialty (orthopedic spine surgery, neurosurgery, and "other" surgery). METHODS A total of 11,601 assessments of upper cervical spine injuries were evaluated based on the AO Spine Upper Cervical Injury Classification System. Reliability and reproducibility scores were obtained twice, with a 3-week time interval. Descriptive statistics were utilized to examine the percentage of accurately classified injuries, and Pearson’s chi-square or Fisher’s exact test was used to screen for potentially relevant differences between study participants. Kappa coefficients (κ) determined the interobserver reliability and intraobserver reproducibility. RESULTS The intraobserver reproducibility was substantial for surgeon experience level (< 5 years: 0.74 vs 5–10 years: 0.69 vs 10–20 years: 0.69 vs > 20 years: 0.70) and surgical subspecialty (orthopedic spine: 0.71 vs neurosurgery: 0.69 vs other: 0.68). Furthermore, the interobserver reliability was substantial for all surgical experience groups on assessment 1 (< 5 years: 0.67 vs 5–10 years: 0.62 vs 10–20 years: 0.61 vs > 20 years: 0.62), and only surgeons with > 20 years of experience did not have substantial reliability on assessment 2 (< 5 years: 0.62 vs 5–10 years: 0.61 vs 10–20 years: 0.61 vs > 20 years: 0.59). Orthopedic spine surgeons and neurosurgeons had substantial intraobserver reproducibility on both assessment 1 (0.64 vs 0.63) and assessment 2 (0.62 vs 0.63), while other surgeons had moderate reliability on assessment 1 (0.43) and fair reliability on assessment 2 (0.36). CONCLUSIONS The international reliability and reproducibility scores for the AO Spine Upper Cervical Injury Classification System demonstrated substantial intraobserver reproducibility and interobserver reliability regardless of surgical experience and spine subspecialty. These results support the global application of this classification system

Energy Minimization through Network Coding for Lifetime Constrained Wireless Networks

Energy management is the key issue in the design and operation of wireless network applications like sensor networks, pervasive computing and ubiquitous computing where the network is primarily driven by battery-powered embedded devices. This paper studies network coding as an energy minimization technique. Network coding reduces the energy consumption by minimizing the number of transmissions required to communicate a given amount of information across the network. However, aggressive application of network coding adversely affects the network lifetime. We illustrate this trade off in this paper, and show that the existing throughput based network coding approaches cannot be applied to energy-constrained networks. Specifically, we address the following routing problem. Given a set of traffic demands the goal is to route the demands across the network with the objective of minimizing the total energy consumption while providing guarantees on the lifetime of individual nodes. This paper studies multi-path variation of the above routing problem. We present analytical formulations to solve the problem optimally. Evaluation results indicate that the proposed solution is 35% more energy efficient than no-network- coding solution while still meeting required lifetime constraints.This is a manuscript of a proceeding published as Gaddam, Nishanth, Sudha Anil Gathala, David Lastine, and Arun Somani. "Energy minimization through network coding for lifetime constrained wireless networks." In 2008 Proceedings of 17th International Conference on Computer Communications and Networks, (2008). DOI: 10.1109/ICCCN.2008.ECP.97. Posted with permission.</p

Energy Minimization through Network Coding for Lifetime Constrained Wireless Networks

Energy management is the key issue in the design and operation of wireless network applications like sensor networks, pervasive computing and ubiquitous computing where the network is primarily driven by battery-powered embedded devices. This paper studies network coding as an energy minimization technique. Network coding reduces the energy consumption by minimizing the number of transmissions required to communicate a given amount of information across the network. However, aggressive application of network coding adversely affects the network lifetime. We illustrate this trade off in this paper, and show that the existing throughput based network coding approaches cannot be applied to energy-constrained networks. Specifically, we address the following routing problem. Given a set of traffic demands the goal is to route the demands across the network with the objective of minimizing the total energy consumption while providing guarantees on the lifetime of individual nodes. This paper studies multi-path variation of the above routing problem. We present analytical formulations to solve the problem optimally. Evaluation results indicate that the proposed solution is 35% more energy efficient than no-network- coding solution while still meeting required lifetime constraints.This is a manuscript of a proceeding published as Gaddam, Nishanth, Sudha Anil Gathala, David Lastine, and Arun Somani. "Energy minimization through network coding for lifetime constrained wireless networks." In 2008 Proceedings of 17th International Conference on Computer Communications and Networks, (2008). DOI: 10.1109/ICCCN.2008.ECP.97. Posted with permission.</p

CODE AWARE DYNAMIC SOURCE ROUTING FOR DISTRIBUTED SENSOR NETWORK

ABSTRACT Sensor network facilitates monitoring and controlling of physical environments. These wireless networks consist of dense collection of sensors capable of collection and dissemination of data. They have application in variety of fields such as military purposes, environment monitoring etc. Typical deployment of sensor network assumes central processing station or a gateway to which all other nodes route their data using dynamic source routing (DSR). This causes congestion at central station and thus reduces the efficiency of the network. In this work we will propose a better dynamic source routing technique using network coding to reduce total number of transmission in sensor networks resulting in better efficiency