Data-Oblivious Graph Algorithms in Outsourced External Memory

Motivated by privacy preservation for outsourced data, data-oblivious external memory is a computational framework where a client performs computations on data stored at a semi-trusted server in a way that does not reveal her data to the server. This approach facilitates collaboration and reliability over traditional frameworks, and it provides privacy protection, even though the server has full access to the data and he can monitor how it is accessed by the client. The challenge is that even if data is encrypted, the server can learn information based on the client data access pattern; hence, access patterns must also be obfuscated. We investigate privacy-preserving algorithms for outsourced external memory that are based on the use of data-oblivious algorithms, that is, algorithms where each possible sequence of data accesses is independent of the data values. We give new efficient data-oblivious algorithms in the outsourced external memory model for a number of fundamental graph problems. Our results include new data-oblivious external-memory methods for constructing minimum spanning trees, performing various traversals on rooted trees, answering least common ancestor queries on trees, computing biconnected components, and forming open ear decompositions. None of our algorithms make use of constant-time random oracles.Comment: 20 page

Distributed linear regression by averaging

Distributed statistical learning problems arise commonly when dealing with large datasets. In this setup, datasets are partitioned over machines, which compute locally, and communicate short messages. Communication is often the bottleneck. In this paper, we study one-step and iterative weighted parameter averaging in statistical linear models under data parallelism. We do linear regression on each machine, send the results to a central server, and take a weighted average of the parameters. Optionally, we iterate, sending back the weighted average and doing local ridge regressions centered at it. How does this work compared to doing linear regression on the full data? Here we study the performance loss in estimation, test error, and confidence interval length in high dimensions, where the number of parameters is comparable to the training data size. We find the performance loss in one-step weighted averaging, and also give results for iterative averaging. We also find that different problems are affected differently by the distributed framework. Estimation error and confidence interval length increase a lot, while prediction error increases much less. We rely on recent results from random matrix theory, where we develop a new calculus of deterministic equivalents as a tool of broader interest.Comment: V2 adds a new section on iterative averaging methods, adds applications of the calculus of deterministic equivalents, and reorganizes the pape

Adaptive Polling for Responsive Web Applications

YesThe web environment has been developing remarkably, and much work has been done towards improving web based notification systems, where servers act smartly by notifying and feeding clients with subscribed data. In this paper we have reviewed some of the problems with current solutions to real-time updates of multi user web applications; we introduce a new concept “adaptive polling” based on one AJAX technique “Polling” to reduce the high volume of redundant server connections with reasonable latency, we demonstrated a prototype implementation of the new concept which is then evaluated against the existing one; the positive results clearly indicated more efficiency in terms of client-server bandwidth

Tight Bounds for Online Matching in Bounded-Degree Graphs with Vertex Capacities

We study the b-matching problem in bipartite graphs G = (S,R,E). Each vertex s ? S is a server with individual capacity b_s. The vertices r ? R are requests that arrive online and must be assigned instantly to an eligible server. The goal is to maximize the size of the constructed matching. We assume that G is a (k,d)-graph [J. Naor and D. Wajc, 2018], where k specifies a lower bound on the degree of each server and d is an upper bound on the degree of each request. This setting models matching problems in timely applications. We present tight upper and lower bounds on the performance of deterministic online algorithms. In particular, we develop a new online algorithm via a primal-dual analysis. The optimal competitive ratio tends to 1, for arbitrary k ? d, as the server capacities increase. Hence, nearly optimal solutions can be computed online. Our results also hold for the vertex-weighted problem extension, and thus for AdWords and auction problems in which each bidder issues individual, equally valued bids. Our bounds improve the previous best competitive ratios. The asymptotic competitiveness of 1 is a significant improvement over the previous factor of 1-1/e^{k/d}, for the interesting range where k/d ? 1 is small. Recall that 1-1/e ? 0.63. Matching problems that admit a competitive ratio arbitrarily close to 1 are rare. Prior results rely on randomization or probabilistic input models

Introducing Parallelism to the Ranges TS

The current interface provided by the C++17 parallel algorithms poses some limitations with respect to parallel data access and heterogeneous systems, such as personal computers and server nodes with GPUs, smartphones, and embedded System on a Chip chipsets. In this paper, we present a summary of why we believe the Ranges TS solves these problems, and also improves both programmability and performance on heterogeneous platforms. The complete paper has been submitted to WG21 for consideration, and here we present a summary of the changes proposed alongside new performance results. To the best of our knowledge, this is the first paper presented to WG21 that unifies the Ranges TS with the parallel algorithms introduced in C++17. Although there are various points of intersection, we will focus on the composability of functions, and the benefit that this brings to accelerator devices via kernel fusion

DESIGN OF RADIUS SERVER ON SERVER NETWORK INTERNET FACULTY OF COMPUTER SCIENCE UNIVERSITY MUHAMMADIYAH METRO

The destination process or otherwise known as routing. Mikrotik Router has provided the management system to hotspot user through separate program package named User Manager. The main problem is the integration of user manager applications into the hardware router Mikrotik considered less effective and flexible because to perform the process of management of the user hotspots must be done on each router located in the hotspot area which will certainly take a relatively long time. From these problems, then created a new system by utilizing external PCRADIUS server as the center of the process of authentication and management of users Mikrotik hotspot. The purpose of this research is to design radius server on internet server network Faculty of Computer Science University Muhammadiyah Metro. While the final results of this study are implementing radius  on the Internet network server Faculty of Computer Science (FIKOM) Universitas Muhammadiyah Metro