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Merzdovnik, G., Huber, M., Buhov, D., Nikiforakis, N., Neuner, S., Schmiedecker, M., Weippl, E..  2017.  Block Me If You Can: A Large-Scale Study of Tracker-Blocking Tools - IEEE Conference Publication.

In this paper, we quantify the effectiveness of third-party tracker blockers on a large scale. First, we analyze the architecture of various state-of-the-art blocking solutions and discuss the advantages and disadvantages of each method. Second, we perform a two-part measurement study on the effectiveness of popular tracker-blocking tools. Our analysis quantifies the protection offered against trackers present on more than 100,000 popular websites and 10,000 popular Android applications. We provide novel insights into the ongoing arms race between trackers and developers of blocking tools as well as which tools achieve the best results under what circumstances. Among others, we discover that rule-based browser extensions outperform learning-based ones, trackers with smaller footprints are more successful at avoiding being blocked, and CDNs pose a major threat towards the future of tracker-blocking tools. Overall, the contributions of this paper advance the field of web privacy by providing not only the largest study to date on the effectiveness of tracker-blocking tools, but also by highlighting the most pressing challenges and privacy issues of third-party tracking.
 

S. Petcy Carolin, M. Somasundaram.  2016.  Data loss protection and data security using agents for cloud environment - IEEE Conference Publication.

Cyber infrastructures are highly vulnerable to intrusions and other threats. The main challenges in cloud computing are failure of data centres and recovery of lost data and providing a data security system. This paper has proposed a Virtualization and Data Recovery to create a virtual environment and recover the lost data from data servers and agents for providing data security in a cloud environment. A Cloud Manager is used to manage the virtualization and to handle the fault. Erasure code algorithm is used to recover the data which initially separates the data into n parts and then encrypts and stores in data servers. The semi trusted third party and the malware changes made in data stored in data centres can be identified by Artificial Intelligent methods using agents. Java Agent Development Framework (JADE) is a tool to develop agents and facilitates the communication between agents and allows the computing services in the system. The framework designed and implemented in the programming language JAVA as gateway or firewall to recover the data loss.
 

Honggang, Zhao, Chen, Shi, Leyu, Zhai.  Submitted.  Design and Implementation of Lightweight 6LoWPAN Gateway Based on Contiki - IEEE Conference Publication.

6LoWPAN technology realizes the IPv6 packet transmission in the IEEE 802.15.4 based WSN. And 6LoWPAN is regarded as one of the ideal technologies to realize the interconnection between WSN and Internet, which is the key to build the IoT. Contiki is an open source and highly portable multitasking operating system, in which the 6LoWPAN has been implemented. In contiki, only several K Bytes of code and a few hundred bytes of memory are required to provide a multitasking environment and built-in TCP/IP support. This makes it especially suitable for memory constrained embedded platforms. In this paper, a lightweight 6LoWPAN gateway based on Contiki is designed and its designs of hardware and software are described. A complex experiment environment is presented, in which the gateway's capability of accessing the Internet is verified, and its performance about the average network delay and jitter are analyzed. The experimental results show that the gateway designed in this paper can not only realize the interconnection between 6LoWPAN networks and Internet, but also have good network adaptability and stability.

Ibrahim, Rosziati, Omotunde, Habeeb.  2017.  A Hybrid Threat Model for Software Security Requirement Specification - IEEE Conference Publication.

Security is often treated as secondary or a non- functional feature of software which influences the approach of vendors and developers when describing their products often in terms of what it can do (Use Cases) or offer customers. However, tides are beginning to change as more experienced customers are beginning to demand for more secure and reliable software giving priority to confidentiality, integrity and privacy while using these applications. This paper presents the MOTH (Modeling Threats with Hybrid Techniques) framework designed to help organizations secure their software assets from attackers in order to prevent any instance of SQL Injection Attacks (SQLIAs). By focusing on the attack vectors and vulnerabilities exploited by the attackers and brainstorming over possible attacks, developers and security experts can better strategize and specify security requirements required to create secure software impervious to SQLIAs. A live web application was considered in this research work as a case study and results obtained from the hybrid models extensively exposes the vulnerabilities deep within the application and proposed resolution plans for blocking those security holes exploited by SQLIAs.
 

Mada, Bharat B., Banik, Manoj, Wu, Bo Chen, Bein, Doina.  2016.  Intrusion Tolerant Multi-cloud Storage - IEEE Conference Publication.

Data generation and its utilization in important decision applications has been growing an extremely fast pace, which has made data a valuable resource that needs to be rigorously protected from attackers. Cloud storage systems claim to offer the promise of secure and elastic data storage services that can adapt to changing storage requirements. Despite diligent efforts being made to protect data, recent successful attacks highlight the need for going beyond the existing approaches centered on intrusion prevention, detection and recovery mechanisms. However, most security mechanisms have finite rate of failure, and with intrusion becoming more sophisticated and stealthy, the failure rate appears to be rising. In this paper we propose the use data fragmentation, followed by coding that introduces redundant fragments and dispersing fragments to multiple and independent cloud storage systems with each cloud handling only a single fragments. The paper proposes a multi-cloud fragmented cloud storage system architecture and design of the related software code. Probabilistic analysis is carried to quantify its intrusion tolerance abilities.
 

Conference Paper
Schrenk, B., Pacher, C..  2018.  1 Gb/s All-LED Visible Light Communication System. 2018 Optical Fiber Communications Conference and Exposition (OFC). :1–3.
We evaluate the use of LEDs intended for illumination as low-cost filtered optical detectors. An optical wireless system that is exclusively based on commercial off-the-shelf 5-mm R/G/B LEDs is experimentally demonstrated for Gb/s close-proximity transmission.
Doerr, Carola, Lengler, Johannes.  2016.  The (1+1) Elitist Black-Box Complexity of LeadingOnes. Proceedings of the Genetic and Evolutionary Computation Conference 2016. :1131–1138.

One important goal of black-box complexity theory is the development of complexity models allowing to derive meaningful lower bounds for whole classes of randomized search heuristics. Complementing classical runtime analysis, black-box models help us understand how algorithmic choices such as the population size, the variation operators, or the selection rules influence the optimization time. One example for such a result is the Ω(n log n) lower bound for unary unbiased algorithms on functions with a unique global optimum [Lehre/Witt, GECCO 2010], which tells us that higher arity operators or biased sampling strategies are needed when trying to beat this bound. In lack of analyzing techniques, almost no non-trivial bounds are known for other restricted models. Proving such bounds therefore remains to be one of the main challenges in black-box complexity theory. With this paper we contribute to our technical toolbox for lower bound computations by proposing a new type of information-theoretic argument. We regard the permutation- and bit-invariant version of LeadingOnes and prove that its (1+1) elitist black-box complexity is Ω(n2), a bound that is matched by (1+1)-type evolutionary algorithms. The (1+1) elitist complexity of LeadingOnes is thus considerably larger than its unrestricted one, which is known to be of order n log log n [Afshani et al., 2013].

Whatmough, P. N., Lee, S. K., Lee, H., Rama, S., Brooks, D., Wei, G. Y..  2017.  14.3 A 28nm SoC with a 1.2GHz 568nJ/prediction sparse deep-neural-network engine with \#x003E;0.1 timing error rate tolerance for IoT applications. 2017 IEEE International Solid-State Circuits Conference (ISSCC). :242–243.

This paper presents a 28nm SoC with a programmable FC-DNN accelerator design that demonstrates: (1) HW support to exploit data sparsity by eliding unnecessary computations (4× energy reduction); (2) improved algorithmic error tolerance using sign-magnitude number format for weights and datapath computation; (3) improved circuit-level timing violation tolerance in datapath logic via timeborrowing; (4) combined circuit and algorithmic resilience with Razor timing violation detection to reduce energy via VDD scaling or increase throughput via FCLK scaling; and (5) high classification accuracy (98.36% for MNIST test set) while tolerating aggregate timing violation rates \textbackslashtextgreater10-1. The accelerator achieves a minimum energy of 0.36μJ/pred at 667MHz, maximum throughput at 1.2GHz and 0.57μJ/pred, or a 10%-margined operating point at 1GHz and 0.58μJ/pred.

Ahmadian, M. M., Shahriari, H. R..  2016.  2entFOX: A framework for high survivable ransomwares detection. 2016 13th International Iranian Society of Cryptology Conference on Information Security and Cryptology (ISCISC). :79–84.

Ransomwares have become a growing threat since 2012, and the situation continues to worsen until now. The lack of security mechanisms and security awareness are pushing the systems into mire of ransomware attacks. In this paper, a new framework called 2entFOX' is proposed in order to detect high survivable ransomwares (HSR). To our knowledge this framework can be considered as one of the first frameworks in ransomware detection because of little publicly-available research in this field. We analyzed Windows ransomwares' behaviour and we tried to find appropriate features which are particular useful in detecting this type of malwares with high detection accuracy and low false positive rate. After hard experimental analysis we extracted 20 effective features which due to two highly efficient ones we could achieve an appropriate set for HSRs detection. After proposing architecture based on Bayesian belief network, the final evaluation is done on some known ransomware samples and unknown ones based on six different scenarios. The result of this evaluations shows the high accuracy of 2entFox in detection of HSRs.

Zong, Fang, Yong, Ouyang, Gang, Liu.  2018.  3D Modeling Method Based on Deep Belief Networks (DBNs) and Interactive Evolutionary Algorithm (IEA). Proceedings of the 2018 International Conference on Big Data and Computing. :124-128.

3D modeling usually refers to be the use of 3D software to build production through the virtual 3D space model with 3D data. At present, most 3D modeling software such as 3dmax, FLAC3D and Midas all need adjust models to get a satisfactory model or by coding a precise modeling. There are many matters such as complicated steps, strong profession, the high modeling cost. Aiming at this problem, the paper presents a new 3D modeling methods which is based on Deep Belief Networks (DBN) and Interactive Evolutionary Algorithm (IEA). Following this method, firstly, extract characteristic vectors from vertex, normal, surfaces of the imported model samples. Secondly, use the evolution strategy, to extract feature vector for stochastic evolution by artificial grading control the direction of evolution, and in the process to extract the characteristics of user preferences. Then, use evolution function matrix to establish the fitness approximation evaluation model, and simulate subjective evaluation. Lastly, the user can control the whole machine simulation evaluation process at any time, and get a satisfactory model. The experimental results show that the method in this paper is feasible.

Vakilinia, I., Tosh, D. K., Sengupta, S..  2017.  3-Way game model for privacy-preserving cybersecurity information exchange framework. MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM). :829–834.

With the growing number of cyberattack incidents, organizations are required to have proactive knowledge on the cybersecurity landscape for efficiently defending their resources. To achieve this, organizations must develop the culture of sharing their threat information with others for effectively assessing the associated risks. However, sharing cybersecurity information is costly for the organizations due to the fact that the information conveys sensitive and private data. Hence, making the decision for sharing information is a challenging task and requires to resolve the trade-off between sharing advantages and privacy exposure. On the other hand, cybersecurity information exchange (CYBEX) management is crucial in stabilizing the system through setting the correct values for participation fees and sharing incentives. In this work, we model the interaction of organizations, CYBEX, and attackers involved in a sharing system using dynamic game. With devising appropriate payoff models for each player, we analyze the best strategies of the entities by incorporating the organizations' privacy component in the sharing model. Using the best response analysis, the simulation results demonstrate the efficiency of our proposed framework.

Carmer, Brent, Malozemoff, Alex J., Raykova, Mariana.  2017.  5Gen-C: Multi-Input Functional Encryption and Program Obfuscation for Arithmetic Circuits. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :747–764.

Program obfuscation is a powerful security primitive with many applications. White-box cryptography studies a particular subset of program obfuscation targeting keyed pseudorandom functions (PRFs), a core component of systems such as mobile payment and digital rights management. Although the white-box obfuscators currently used in practice do not come with security proofs and are thus routinely broken, recent years have seen an explosion of cryptographic techniques for obfuscation, with the goal of avoiding this build-and-break cycle. In this work, we explore in detail cryptographic program obfuscation and the related primitive of multi-input functional encryption (MIFE). In particular, we extend the 5Gen framework (CCS 2016) to support circuit-based MIFE and program obfuscation, implementing both existing and new constructions. We then evaluate and compare the efficiency of these constructions in the context of PRF obfuscation. As part of this work we (1) introduce a novel instantiation of MIFE that works directly on functions represented as arithmetic circuits, (2) use a known transformation from MIFE to obfuscation to give us an obfuscator that performs better than all prior constructions, and (3) develop a compiler for generating circuits optimized for our schemes. Finally, we provide detailed experiments, demonstrating, among other things, the ability to obfuscate a PRF with a 64-bit key and 12 bits of input (containing 62k gates) in under 4 hours, with evaluation taking around 1 hour. This is by far the most complex function obfuscated to date.

Guri, M., Mirsky, Y., Elovici, Y..  2017.  9-1-1 DDoS: Attacks, Analysis and Mitigation. 2017 IEEE European Symposium on Security and Privacy (EuroS P). :218–232.

The 911 emergency service belongs to one of the 16 critical infrastructure sectors in the United States. Distributed denial of service (DDoS) attacks launched from a mobile phone botnet pose a significant threat to the availability of this vital service. In this paper we show how attackers can exploit the cellular network protocols in order to launch an anonymized DDoS attack on 911. The current FCC regulations require that all emergency calls be immediately routed regardless of the caller's identifiers (e.g., IMSI and IMEI). A rootkit placed within the baseband firmware of a mobile phone can mask and randomize all cellular identifiers, causing the device to have no genuine identification within the cellular network. Such anonymized phones can issue repeated emergency calls that cannot be blocked by the network or the emergency call centers, technically or legally. We explore the 911 infrastructure and discuss why it is susceptible to this kind of attack. We then implement different forms of the attack and test our implementation on a small cellular network. Finally, we simulate and analyze anonymous attacks on a model of current 911 infrastructure in order to measure the severity of their impact. We found that with less than 6K bots (or \$100K hardware), attackers can block emergency services in an entire state (e.g., North Carolina) for days. We believe that this paper will assist the respective organizations, lawmakers, and security professionals in understanding the scope of this issue in order to prevent possible 911-DDoS attacks in the future.

Le, Van-Khoa, Beauseroy, Pierre, Grall-Maes, Edith.  2018.  Abnormal Trajectory Detection for Security Infrastructure. Proceedings of the 2Nd International Conference on Digital Signal Processing. :1–5.
In this work, an approach for the automatic analysis of people trajectories is presented, using a multi-camera and card reader system. Data is first extracted from surveillance cameras and card readers to create trajectories which are sequences of paths and activities. A distance model is proposed to compare sequences and calculate similarities. The popular unsupervised model One-Class Support Vector Machine (One-Class SVM) is used to train a detector. The proposed method classifies trajectories as normal or abnormal and can be used in two modes: off-line and real-time. Experiments are based on data simulation corresponding to an attack scenario proposed by a security expert. Results show that the proposed method successfully detects the abnormal sequences in the scenario with very low false alarm rate.
Sharma, Manoj Kumar, Sheet, Debdoot, Biswas, Prabir Kumar.  2016.  Abnormality Detecting Deep Belief Network. Proceedings of the International Conference on Advances in Information Communication Technology & Computing. :11:1–11:6.

Abnormality detection is useful in reducing the amount of data to be processed manually by directing attention to the specific portion of data. However, selections of suitable features are important for the success of an abnormality detection system. Designing and selecting appropriate features are time-consuming, requires expensive domain knowledge and human labor. Further, it is very challenging to represent high-level concepts of abnormality in terms of raw input. Most of the existing abnormality detection system use handcrafted feature detector and are based on shallow architecture. In this work, we explore Deep Belief Network for abnormality detection and simultaneously, compared the performance of classic neural network in terms of features learned and accuracy of detecting the abnormality. Further, we explore the set of features learn by each layer of the deep architecture. We also provide a simple and fast mechanism to visualize the feature at the higher layer. Further, the effect of different activation function on abnormality detection is also compared. We observed that deep learning based approach can be used for detecting an abnormality. It has better performance compare to classical neural network in separating distinct as well as almost similar data.

Abi-Antoun, Marwan, Khalaj, Ebrahim, Vanciu, Radu, Moghimi, Ahmad.  2016.  Abstract Runtime Structure for Reasoning About Security: Poster. Proceedings of the Symposium and Bootcamp on the Science of Security. :1–3.

We propose an interactive approach where analysts reason about the security of a system using an abstraction of its runtime structure, as opposed to looking at the code. They interactively refine a hierarchical object graph, set security properties on abstract objects or edges, query the graph, and investigate the results by studying highlighted objects or edges or tracing to the code. Behind the scenes, an inference analysis and an extraction analysis maintain the soundness of the graph with respect to the code.

Badii, A., Faulkner, R., Raval, R., Glackin, C., Chollet, G..  2017.  Accelerated Encryption Algorithms for Secure Storage and Processing in the Cloud. 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). :1–6.

The objective of this paper is to outline the design specification, implementation and evaluation of a proposed accelerated encryption framework which deploys both homomorphic and symmetric-key encryptions to serve the privacy preserving processing; in particular, as a sub-system within the Privacy Preserving Speech Processing framework architecture as part of the PPSP-in-Cloud Platform. Following a preliminary study of GPU efficiency gains optimisations benchmarked for AES implementation we have addressed and resolved the Big Integer processing challenges in parallel implementation of bilinear pairing thus enabling the creation of partially homomorphic encryption schemes which facilitates applications such as speech processing in the encrypted domain on the cloud. This novel implementation has been validated in laboratory tests using a standard speech corpus and can be used for other application domains to support secure computation and privacy preserving big data storage/processing in the cloud.

Wang, X., Zhou, S..  2017.  Accelerated Stochastic Gradient Method for Support Vector Machines Classification with Additive Kernel. 2017 First International Conference on Electronics Instrumentation Information Systems (EIIS). :1–6.

Support vector machines (SVMs) have been widely used for classification in machine learning and data mining. However, SVM faces a huge challenge in large scale classification tasks. Recent progresses have enabled additive kernel version of SVM efficiently solves such large scale problems nearly as fast as a linear classifier. This paper proposes a new accelerated mini-batch stochastic gradient descent algorithm for SVM classification with additive kernel (AK-ASGD). On the one hand, the gradient is approximated by the sum of a scalar polynomial function for each feature dimension; on the other hand, Nesterov's acceleration strategy is used. The experimental results on benchmark large scale classification data sets show that our proposed algorithm can achieve higher testing accuracies and has faster convergence rate.

Perry, David M., Mattavelli, Andrea, Zhang, Xiangyu, Cadar, Cristian.  2017.  Accelerating Array Constraints in Symbolic Execution. Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis. :68–78.

Despite significant recent advances, the effectiveness of symbolic execution is limited when used to test complex, real-world software. One of the main scalability challenges is related to constraint solving: large applications and long exploration paths lead to complex constraints, often involving big arrays indexed by symbolic expressions. In this paper, we propose a set of semantics-preserving transformations for array operations that take advantage of contextual information collected during symbolic execution. Our transformations lead to simpler encodings and hence better performance in constraint solving. The results we obtain are encouraging: we show, through an extensive experimental analysis, that our transformations help to significantly improve the performance of symbolic execution in the presence of arrays. We also show that our transformations enable the analysis of new code, which would be otherwise out of reach for symbolic execution.

Jaeger, D., Cheng, F., Meinel, C..  2018.  Accelerating Event Processing for Security Analytics on a Distributed In-Memory Platform. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). :634-643.

The analysis of security-related event logs is an important step for the investigation of cyber-attacks. It allows tracing malicious activities and lets a security operator find out what has happened. However, since IT landscapes are growing in size and diversity, the amount of events and their highly different representations are becoming a Big Data challenge. Unfortunately, current solutions for the analysis of security-related events, so called Security Information and Event Management (SIEM) systems, are not able to keep up with the load. In this work, we propose a distributed SIEM platform that makes use of highly efficient distributed normalization and persists event data into an in-memory database. We implement the normalization on common distribution frameworks, i.e. Spark, Storm, Trident and Heron, and compare their performance with our custom-built distribution solution. Additionally, different tuning options are introduced and their speed advantage is presented. In the end, we show how the writing into an in-memory database can be tuned to achieve optimal persistence speed. Using the proposed approach, we are able to not only fully normalize, but also persist more than 20 billion events per day with relatively small client hardware. Therefore, we are confident that our approach can handle the load of events in even very large IT landscapes.

Duraisamy, Karthi, Lu, Hao, Pande, Partha Pratim, Kalyanaraman, Ananth.  2017.  Accelerating Graph Community Detection with Approximate Updates via an Energy-Efficient NoC. Proceedings of the 54th Annual Design Automation Conference 2017. :89:1–89:6.

Community detection is an advanced graph operation that is used to reveal tightly-knit groups of vertices (aka. communities) in real-world networks. Given the intractability of the problem, efficient heuristics are used in practice. Yet, even the best of these state-of-the-art heuristics can become computationally demanding over large inputs and can generate workloads that exhibit inherent irregularity in data movement on manycore platforms. In this paper, we posit that effective acceleration of the graph community detection operation can be achieved by reducing the cost of data movement through a combined innovation at both software and hardware levels. More specifically, we first propose an efficient software-level parallelization of community detection that uses approximate updates to cleverly exploit a diminishing returns property of the algorithm. Secondly, as a way to augment this innovation at the software layer, we design an efficient Wireless Network on Chip (WiNoC) architecture that is suited to handle the irregular on-chip data movements exhibited by the community detection algorithm under both unicast- and broadcast-heavy cache coherence protocols. Experimental results show that our resulting WiNoC-enabled manycore platform achieves on average 52% savings in execution time, without compromising on the quality of the outputs, when compared to a traditional manycore platform designed with a wireline mesh NoC and running community detection without employing approximate updates.

Choi, Jongsok, Lian, Ruolong, Li, Zhi, Canis, Andrew, Anderson, Jason.  2018.  Accelerating Memcached on AWS Cloud FPGAs. Proceedings of the 9th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies. :2:1–2:8.
In recent years, FPGAs have been deployed in data centres of major cloud service providers, such as Microsoft [1], Amazon [2], Alibaba [3], Tencent [4], Huawei [5], and Nimbix [6]. This marks the beginning of bringing FPGA computing to the masses, as being in the cloud, one can access an FPGA from anywhere. A wide range of applications are run in the cloud, including web servers and databases among many others. Memcached is a high-performance in-memory ob ject caching system, which acts as a caching layer between web servers and databases. It is used by many companies, including Flicker, Wikipedia, Wordpress, and Facebook [7, 8]. In this paper, we present a Memcached accelerator implemented on the AWS FPGA cloud (F1 instance). Compared to AWS ElastiCache, an AWS-managed CPU Memcached service, our Memcached accelerator provides up to 9 x better throughput and latency. A live demo of the Memcached accelerator running on F1 can be accessed on our website [9].
Bai, Xu, Jiang, Lei, Dai, Qiong, Yang, Jiajia, Tan, Jianlong.  2017.  Acceleration of RSA processes based on hybrid ARM-FPGA cluster. 2017 IEEE Symposium on Computers and Communications (ISCC). :682–688.

Cooperation of software and hardware with hybrid architectures, such as Xilinx Zynq SoC combining ARM CPU and FPGA fabric, is a high-performance and low-power platform for accelerating RSA Algorithm. This paper adopts the none-subtraction Montgomery algorithm and the Chinese Remainder Theorem (CRT) to implement high-speed RSA processors, and deploys a 48-node cluster infrastructure based on Zynq SoC to achieve extremely high scalability and throughput of RSA computing. In this design, we use the ARM to implement node-to-node communication with the Message Passing Interface (MPI) while use the FPGA to handle complex calculation. Finally, the experimental results show that the overall performance is linear with the number of nodes. And the cluster achieves 6× 9× speedup against a multi-core desktop (Intel i7-3770) and comparable performance to a many-core server (288-core). In addition, we gain up to 2.5× energy efficiency compared to these two traditional platforms.

Oharada, Kazuya, Shizuki, Buntarou, Takahashi, Shin.  2017.  AccelTag: A Passive Smart ID Tag with Acceleration Sensor for Interactive Applications. Adjunct Publication of the 30th Annual ACM Symposium on User Interface Software and Technology. :63–64.

There are many everyday situations in which users need to enter their user identification (user ID), such as logging in to computer systems and entering secure offices. In such situations, contactless passive IC cards are convenient because users can input their user ID simply by passing the card over a reader. However, these cards cannot be used for successive interactions. To address this issue, we propose AccelTag, a contactless IC card equipped with an acceleration sensor and a liquid crystal display (LCD). AccelTag utilizes high-function RFID technology so that the acceleration sensor and the LCD can also be driven by a wireless power supply. With its built-in acceleration sensor, AccelTag can acquire its direction and movement when it is waved over the reader. We demonstrate several applications using AccelTag, such as displaying several types of information in the card depending on the user's requirements.

Tan, Gaosheng, Zhang, Rui, Ma, Hui, Tao, Yang.  2017.  Access Control Encryption Based on LWE. Proceedings of the 4th ACM International Workshop on ASIA Public-Key Cryptography. :43–50.

Damgard et al. proposed a new primitive called access control encryption (ACE) [6] which not only protects the privacy of the message, but also controls the ability of the sender to send the message. We will give a new construction based on the Learning with Error (LWE) assumption [12], which is one of the two open problems in [6]. Although there are many public key encryption schemes based on LWE and supporting homomorphic operations. We find that not every scheme can be used to build ACE. In order to keep the security and correctness of ACE, the random constant chosen by the sanitizer should satisfy stricter condition. We also give a different security proof of ACE based on LWE from it based on DDH. We will see that although the modulus of LWE should be super-polynomial, the ACE scheme is still as secure as the general public key encryption scheme based on the lattice [5].