We present a survey of authentication and key agreement schemes that are proposed for the SIP protocol. SIP has become the center piece for most VoIP architectures. Performance and security of the authentication and key agreement schemes are two critical factors that affect the VoIP applications with large number of users. Therefore, we have identified, categorized and evaluated various SIP authentication and key agreement protocols according to their performance and security features. Although the performance is inversely proportional to the security features provided in general, we observed that there are successful schemes from both the performance and security viewpoint.

A Survey of SIP Authentication and Key Agreement Schemes

The development of the Internet of Things (IoT) is predicted to change the healthcare industry and might lead to the rise of the Internet of Medical Things. The IoT revolution is surpassing the present-day human services with promising mechanical, financial, and social prospects. This paper investigated the security of medical images in IoT by utilizing an innovative cryptographic model with optimization strategies. For the most part, the patient data are stored as a cloud server in the hospital due to which the security is vital. So another framework is required for the secure transmission and effective storage of medical images interleaved with patient information. For increasing the security level of encryption and decryption process, the optimal key will be chosen using hybrid swarm optimization, i.e., grasshopper optimization and particle swarm optimization in elliptic curve cryptography. In view of this method, the medical images are secured in IoT framework. From this execution, the results are compared and contrasted, whereas a diverse encryption algorithm with its optimization methods from the literature is identified with the most extreme peak signal-to-noise ratio values, i.e., 59.45 dB and structural similarity index as 1.

Hybrid optimization with cryptography encryption for medical image security in Internet of Things

The Cancer Genomics Hub (CGHub) is the online repository of the sequencing programs of the National Cancer Institute (NCI), including The Cancer Genomics Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects, with data from 25 different types of cancer. The CGHub currently contains >1.4 PB of data, has grown at an average rate of 50 TB a month and serves >100 TB per week. The architecture of CGHub is designed to support bulk searching and downloading through a Web-accessible application programming interface, enforce patient genome confidentiality in data storage and transmission and optimize for efficiency in access and transfer. In this article, we describe the design of these three components, present performance results for our transfer protocol, GeneTorrent, and finally report on the growth of the system in terms of data stored and transferred, including estimated limits on the current architecture. Our experienced-based estimates suggest that centralizing storage and computational resources is more efficient than wide distribution across many satellite labs. Database URL: https://cghub.ucsc.edu.

/pdf/the-cancer-genomics-hub-cghub-overcoming-cancer-through-the-2die3qhde0.pdf

The Cancer Genomics Hub (CGHub): overcoming cancer through the power of torrential data

Machine learning has become mainstream across industries. Numerous examples prove the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using side-channel information such as timing and electromagnetic (EM) emanations. To this end, we consider multilayer perceptron and convolutional neural networks as the machine learning architectures of choice and assume a non-invasive and passive attacker capable of measuring those kinds of leakages. We conduct all experiments on real data and commonly used neural network architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM Cortex-M3 microcontroller, which is a platform often used in pervasive applications using neural networks such as wearables, surveillance cameras, etc. Our experiments show that a side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using merely side-channel information such as timing or EM.

/pdf/csi-nn-reverse-engineering-of-neural-network-architectures-5grdl0uwn7.pdf

{CSI} {NN}: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel

With the growth of the Internet comes a growth in a ubiquitous networked society. Common Web 2.0 applications include a rapidly growing trend for social network sites. Social network sites typically converged different relationship types into one group of “friends.” However, with such vast interconnectivity, convergence of relationships, and information sharing by individual users comes an increased risk of privacy violations. We asked a small sample of participants to discuss what friendship and privacy meant to them and to give examples of a privacy violation they had experienced. A thematic analysis was conducted on the interviews to determine the issues discussed by the participants. Many participants experienced privacy issues using the social network site Facebook. The results are presented here and discussed in relation to online privacy concerns, notably social network site privacy concerns and managing such information.

https://www.tandfonline.com/doi/pdf/10.1080/15228831003770775

Privacy, Social Network Sites, and Social Relations

Security is always a major concern in the field of communication. Advanced encryption standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms are the two popular encryption schemes that guarantee confidentiality and authenticity over an insecure communication channel. There has been trifling cryptanalytic progress against these two algorithms since their advent. This paper presents the fundamental mathematics behind the AES and RSA algorithm along with a brief description of some cryptographic primitives that are commonly used in the field of communication security. It also includes several computational issues as well as the analysis of AES and RSA security aspects against different kinds of attacks including the countermeasures against these attacks.

A Comparative Study of the Performance and Security Issues of AES and RSA Cryptography

Summary In the recent years, we have witnessed a dramatic rise in popularity of online social networking services, with several Social Network Sites (SNSs) such as Myspace, Facebook, Blogger, You Tube, Yahoo! Groups etc are now among the most visited websites globally. However, since such forums are relatively easy to access and the users are often not aware of the size and the nature of the audience accessing their profiles, they often reveal more information which is not appropriate to a public forum. As a result, such commercial and social site may often generate a number of privacy and security related threats for the members. This paper highlights the commercial and social benefits of safe and well-informed use of SNSs and emphasizes the most important threats to users of SNSs as well as illustrates the fundamental factors behind these threats. It also presents policy and technical recommendations to improve privacy and security without compromising the benefits of information sharing through SNSs.

/pdf/threats-of-online-social-networks-1ijtv4xoiz.pdf

Threats of Online Social Networks

The increasing integration of new technologies for power generation into the smart grid systems calls for novel demand response (DR) algorithms, which schedule the appliances to minimize cost and maximize comfort for the users. Traditionally, the formulations of the DR problem consider unidirectional energy flow from the power grid (energy supplier) to the user in a residential building (energy consumer). In this paper, we argue for an extended model of a smart residential building with bidirectional energy trading. This model allows the user to sell the surplus energy, obtained from local renewable energy sources, so as to partially recover the electricity cost. We further study an efficient linear model for appliance scheduling, under the assumption of bidirectional energy trading. To balance the user comfort and electricity cost, we introduce a comfort demand function based on declining block rates (DBR) and discuss the microeconomic meaning of this function. We evaluate our method with several case studies and analyze how energy selling and comfort demand affect the total cost and the schedule. We also show that our scheduler is fast enough to allow for nearly realtime scheduling adjustments ahead of each period, to minimize the impact of forecast deviations.

Cost-comfort balancing in a smart residential building with bidirectional energy trading

In this paper, we present three performance and energy case studies of benchmark applications in the OmpSs environment for task based programming. Different parallel and vectorized implementations are evaluated on an Intel® CoreTMi7-2600 quad-core processor. Using FLOPS/W derived from chip MSR registers, we find AVX code to be clearly most energy efficient in general. The peak on-chip GFLOPS/W rates are: Black-Scholes (BS) 0.89, FFTW 1.38 and Matrix Multiply (MM) 1.97. Experiments cover variable degrees of thread parallelism and different OmpSs task pool scheduling policies. We find that maximum energy efficiency for small and medium sized problems is obtained by limiting the number of parallel threads. Comparison of AVX variants with non-vectorized code shows ≈6−7 × (BS) and ≈3−5 × (FFTW) improvements in on-chip energy efficiency, depending on the problem size and degree of multithreading.

Case studies of multi-core energy efficiency in task based programs

Chip multiprocessors (CMPs) and heterogeneous architectures have become predominant in all market segments, from embedded to high performance computing. These architectures exacerbate on-chip data requirements, creating additional pressure on the memory subsystem. Consequently, efficient utilization of on-chip memory space becomes critical for data intensive applications. A promising means of addressing this challenge is to use an effective compression method to reduce the data transmitted along the memory hierarchy. In this paper we present V-PFORDelta, a real-time vectorized integer differential compression method for memory bound applications. We evaluate the effectiveness of our SIMD (Single Instruction Multiple Data stream) based compression method on an industrial hydrological time series data processing kernel. We analyzed both Streaming SIMD Extensions (SSE) and Advanced Vector Extensions 2 (AVX2) versions of the compression method. Results show that the performance and energy efficiency can be improved up to a factor of 3.1 and 8.2, respectively. The proposed method not only outperforms the uncompressed SIMD implementations of the hydrological kernel, but also reduces the data storage requirements by a factor of 1.56x to 3.38x, depending on the analyzed dataset.

Abdullah Al Hasib

Papers

A Comparative Study of the Performance and Security Issues of AES and RSA Cryptography

Threats of Online Social Networks

Cost-comfort balancing in a smart residential building with bidirectional energy trading

Case studies of multi-core energy efficiency in task based programs

V-PFORDelta: Data Compression for Energy Efficient Computation of Time Series