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A recent survey on challenges in security and privacy in internet of things 19

A recent survey on challenges in security and privacy in int...

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5th International conference on Engineering and MIS, ICEMIS 2019

作者： Aljawarneh, Shadi Radhakrishna, Vangipuram Kumar, Gunupudi Rajesh Department of Software Engineering Jordan University of Science and Technology Irbid22110 Jordan Department of Information Technology VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9781450372121

Computing environment in IoT (Internet of things) is surrounded with huge amounts of heterogeneous data fulfilling many services in everyone's daily life. Since, communication process in IoT takes place using different devices such as smart phones, sensors, mobile devices, household devices, embedded equipment etc. With the use of these variety of devices, the exchange of data in open internet environment is prone to vulnerabilities. the main cause for these vulnerabilities is the weaknesses in the design of software components and hardware components. Bridging communications gaps in the IoT is a complex process as the data is from heterogeneous sources. An effort is made in this paper to discuss various challenges that are being faced in security and privacy of data. this will be very much helpful for researchers who want to pursue research. © 2019 Association for Computing Machinery.

关键词： Internet of things

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dataBlinder: A distributed data protection middleware supporting search and computation on encrypted data 19

DataBlinder: A distributed data protection middleware suppor...

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20th International Middleware conference Industrial Track (Middleware Industry)

作者： Heydari, Emad Lagaisse, Bert Joosen, Wouter Aly, Abdelrahaman Brackx, Michael Katholieke Univ Leuven Imec DistriNet Leuven Belgium Katholieke Univ Leuven Imec COSIC Leuven Belgium UnifiedPost La Hulpe Belgium

ISBN: (纸本)9781450370417

Business application owners want to outsource data storage, including sensitive data, to the public cloud for economical reasons. this is often challenging since these businesses are and remain responsible for regulatory compliance and data protection, even though cloud providers may do their best to offer (data) protection. Meanwhile, data protection techniques evolve and get better because of continuous research and improvement of advanced encryption. Numerous cryptographic tactics have been proposed, e.g., searchable symmetric encryption (SSE) and homomorphic encryption (HE), that support search and aggregation functions on encrypted data. Each of these tactics has a trade-off between security, performance and functionality, but there is no one-size-fits-all solution. For the application developer, the underpinning concepts of these tactics are complex to comprehend, complex to integrate in a distributed application, and prone to implementation mistakes. In this paper we present dataBlinder, a distributed data access middleware that provides crypto agility by means of configurable fine-grained data protection at the application level. dataBlinder supports adaptive runtime selection of data protection tactics, and offers a plugin architecture for such tactics based on a key abstraction model for protection level, performance and supported query functionality. We have developed this middleware in close collaboration with businesses that face these challenges and offer cloud-based applications in e-finance, and e-health, by implementing and integrating state-of-the-art cryptographic schemes to dataBlinder. this paper illustrates the case of medical data protection with FHIR-compliant [30] medical data.

关键词： security and privacy middleware data protection

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Machine learning based cloudbot detection using multi-layer traffic statistics 21

Machine learning based cloudbot detection using multi-layer ...

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21st IEEE International conference on High Performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Guo, Yu Shi, Junzheng Cao, Zigang Kang, Cuicui Xiong, Gang Li, Zhen Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728120584

With the rapid development of e-commerce services and online transactions, an increasing number of advanced web robots are utilized by speculators and hackers in underground economy to perform click fraud, register fake accounts and commit other kinds of frauds, seriously harming the profit of businesses and the fairness of online activities. there is solid evidence that the vast majority of such malicious bot traffic comes from data centers. the malicious bot deployed on the hosts of data centers is referred to as a CloudBot. How to detect and block CloudBots effectively has become an urgent problem in practice, while the research on it can be seldom seen in public. To this end, we propose a traffic-based quasi-real-time method for CloudBot detection using machine learning, which exploits a new sample partitioning approach, as well as innovative multi-layer features that reveal the essential difference between CloudBots and human traffic. Our method achieves 93.4% precision in the experiment and performs well on the real-world dataset, which proves to be effective to detect unknown CloudBots and combat the concept drift caused by varying time. Besides, the approach is also privacy-preserving without using any specific application layer information. We believe our work can benefit network economy security and fairness in practice. © 2019 IEEE.

关键词： Feature extraction

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Secure Fine-Grained data Access Control Over Multiple Cloud Server Based Healthcare applications 5

Secure Fine-Grained Data Access Control Over Multiple Cloud ...

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5th International conference on Computing, Communication Control and Automation (ICCUBEA)

作者： Deshmukh, Nilam Manikrao Kumar, Santosh Shirsath, Rakesh Sandip Inst Technol & Res Ctr SPPU Dept Comp Engn Nasik India

ISBN: (纸本)9781728140421

With the flourishing build out of the cloud computing, privacy and security worries of outsourcing data have been increasing to a great extent. Mobile cloud model aids for analyzing the information related with patients data and to give recommendations in healthcare applications. A fine-grained level access management of multi-server cloud information is very essential requirement accomplishing and for implementation of end users application, in mobile cloud computing. Present paper is to illustrate collective focus of fine grained access control of clouds based servers information besides a incontrovertibly mobile cloud users validation system of healthcare industry using ABE algorithm. We as a proposed system will use an auditing technique for fraud detection of data on cloud side. Initially distributed modular and distinguished construction of large-scale virtual mechanisms behavior is illustrated based on distributed monitoring agent and later on feasible, appropriate parallel trust computing system is presented for large data. Evaluation of analysis and results of the experiment confirms the practicability and efficiency of system proposed.

关键词： distributed mobile cloud computing healthcare industry 4.0 fine-grained access control user authentication attribute-based encryption security

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Ramparts: A Programmer-Friendly System for Building Homomorphic Encryption applications 7

Ramparts: A Programmer-Friendly System for Building Homomorp...

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7th acm Workshop on Encrypted Computing and Applied Homomorphic Cryptography (WAHC)

作者： Archer, David W. Trilla, Jose Manuel Calderon Dagit, Jason Malozemoff, Alex J. Polyakov, Yuriy Rohloff, Kurt Ryan, Gerard Galois Inc Portland OR 97204 USA New Jersey Inst Technol Newark NJ 07102 USA

ISBN: (纸本)9781450368292

Homomorphic Encryption (HE) is an emerging technology that enables computing on data while the data is encrypted. A major challenge with homomorphic encryption is that it takes extensive expert knowledge to design meaningful and useful programs that are constructed from atomic HE operations. We present Ramparts to address this challenge. Ramparts provides an environment for developing HE applications in Julia, a high-level language, the same way as "cleartext" applications are typically written in Julia. Ramparts makes the following three contributions. First, we use symbolic execution to automate the construction of an optimized computation circuit where both the circuit size and multiplicative depth are chosen by the compiler. Second, Ramparts automatically selects the HE parameters for the generated circuit, which is typically done manually by an HE expert. third, Ramparts automatically selects the plaintext encoding for input values, and performs input and output data transformations. these three operations are not easily performed by programmers who are not HE experts. thus, Ramparts makes HE more widely available and usable by the the population of programmers. We compare our approach with Cingulata, the only previously known system that automatically generates circuits for HE computations. the HE circuits generated by Ramparts are significantly more efficient than the circuits compiled by Cingulata. For instance, our runtimes for key generation/circuit compilation and all online operations are more than one order of magnitude lower for a sample image processing application used for performance evaluation in our study.

关键词： Homomorphic encryption Automatic arithmetic circuit generation Lattice-based cryptography Usable security and privacy

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IoT Healthcare: Benefits, Issues and Challenges 19

IoT Healthcare: Benefits, Issues and Challenges

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5th EAI International conference on Smart Objects and Technologies for Social Good (GOODTECHS)

作者： De Michele, Roberta Furini, Marco Univ Bologna Dept Ind Engn Bologna Italy Univ Modena & Reggio Emilia Dip Comunicaz Econ Reggio Emilia Italy

ISBN: (纸本)9781450362610

A sportsman's watch no longer marks the time, but it also provides information on the geographical location, on the number of steps, on the heart rate, on the blood oxygenation, on the blood pressure, etc. the bracelet of an elderly person can measure different physiological signals and can transmit them in real time to her doctor, allowing him to monitor the state of health even if the two are not in the same place. thanks to the implementation and use of sensors, watches and bracelets have become smart objects. It is the world of the Internet of things that enters the health sector and fills the shelves of electronic stores with objects that can monitor our bodies. A market sector that is worth billions of dollars, but that has several implications along with its benefits. What happens to our data? Who has access to our data? Is it possible to cyber-attack these devices? Is our behavior influenced by reading this data?these are some of the questions that arise from the use of IoT devices in the health sector and in this paper we will address them by analyzing this scenario in detail, by highlighting threats and vulnerabilities and by proposing approaches that might mitigate issues related to the use of IoT in the health scenario.

关键词： IoT healthcare privacy security

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HP-SDDAN: High-performance software-defined data access network 21

HP-SDDAN: High-performance software-defined data access netw...

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作者： You, Ruibang Yuan, Zimu Tu, Bibo Cheng, Jie Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781728120584

Recently, data protection has become increasingly important in cloud environments. the cloud platform has global user information, rich storage resource allocation information, and a fuller understanding of data attributes. At the same time, there is an urgent need for data access control to provide data security, and software-defined network, as a ready-made facility, has a global network view, global network management capabilities, and programable network rules. In this paper, we present an approach, named High-Performance Software-Defined data Access Network (HP-SDDAN), providing software-defined data access network architecture, global data attribute management and attribute-based data access network. HP-SDDAN combines the excellent features of cloud platform and software-defined network, and fully considers the performance to implement software-defined data access network. In evaluation, we verify the effectiveness and efficiency of HP-SDDAN implementation, with only 1.46% overhead to achieve attribute-based data access control of attribute-based differential privacy. © 2019 IEEE.

关键词： data privacy

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Recurrent neural network based password generation for group attribute context-ware applications 21

Recurrent neural network based password generation for group...

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作者： Luo, Jun Deng, Jin Lu, Chu Liu, Hong Third Research Institute Ministry of Public Security China Beijing Traffic Management Bureau Beijing Public Security Bureau China East China Normal University China

ISBN: (纸本)9781728120584

Password generation becomes an emerging issue to improve the efficiency of authentication in social engineering and plays an important role in checking the security vulnerability since the weak password may lead to possible attacks for different users. To determine the password vulnerability and to enhance user privacy, a recurrent neural network (i.e., long short-term memory (LSTM) and gated recurrent unit (GRU)) based password generation scheme is proposed for group attribute context-ware applications. In this work, different group attributes are considered for modeling training, natural language processing is adopted for password prediction, and password similarity computing is established for analyzing the group relations. the password strength evaluation is performed on the fixed length password based on generation probability. It indicates that group attributes based password generation model has more accuracy than the ordinary model. © 2019 IEEE.

关键词： Long short-term memory

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nGraph-HE2: A High-throughput Framework for Neural Network Inference on Encrypted data 7

nGraph-HE2: A High-Throughput Framework for Neural Network I...

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7th acm Workshop on Encrypted Computing and Applied Homomorphic Cryptography (WAHC)

作者： Boemer, Fabian Costache, Anamaria Cammarota, Rosario Wierzynski, Casimir Intel AI Res San Diego CA 92129 USA

ISBN: (纸本)9781450368292

In previous work, Boemer et al. introduced nGraph-HE, an extension to the Intel nGraph deep learning (DL) compiler, that enables data scientists to deploy models with popular frameworks such as TensorFlow and PyTorch with minimal code changes. However, the class of supported models was limited to relatively shallow networks with polynomial activations. Here, we introduce nGraph-HE2, which extends nGraph-HE to enable privacy-preserving inference on standard, pre-trained models using their native activation functions and number fields (typically real numbers). the proposed framework leverages the CKKS scheme, whose support for real numbers is friendly to data science, and a client-aided model using a two-party approach to compute activation functions. We first present CKKS-specific optimizations, enabling a 3x-88x runtime speedup for scalar encoding, and doubling the throughput through a novel use of CKKS plaintext packing into complex numbers. Second, we optimize ciphertext-plaintext addition and multiplication, yielding 2.6x-4.2x runtime speedup. third, we exploit two graph-level optimizations: lazy rescaling and depth-aware encoding, which allow us to significantly improve performance. Together, these optimizations enable state-of-the-art throughput of 1,998 images/s on the CryptoNets network. Using the client-aided model, we also present homomorphic evaluation of (to our knowledge) the largest network to date, namely, pre-trained MobileNetV2 models on the ImageNet dataset, with 60.4%/82.7% top-1/top-5 accuracy and an amortized runtime of 381 ms/image.

关键词： privacy-Preserving Machine Learning Deep Learning Graph Compilers Homomorphic Encryption

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5th International conference on Dependability in Sensor, Cloud, and Big data Systems and applications, DependSys 2019

5th International Conference on Dependability in Sensor, Clo...

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5th International conference on Dependability in Sensor, Cloud, and Big data Systems and applications, DependSys 2019

ISBN: (纸本)9789811513039

the proceedings contain 39 papers. the special focus in this conference is on Dependability in Sensor, Cloud, and Big data Systems and applications. the topics include: Assessing the Dependability of Apache Spark System: Streaming Analytics on Large-Scale Ocean data;on the Assessment of security and Performance Bugs in Chromium Open-Source Project;medical Image Segmentation by Combining Adaptive Artificial Bee Colony and Wavelet Packet Decomposition;recommender System for Decentralized Cloud Manufacturing;gait Analysis for Gender Classification in Forensics;hybrid Cloud Computing Architecture Based on Open Source Technology;transportation and Charging Schedule for Autonomous Electric Vehicle Riding-Sharing System Considering Battery Degradation;blockchain-Powered Service Migration for Uncertainty-Aware Workflows in Edge Computing;towards the Design of a Covert Channel by Using Web Tracking Technologies;CodeeGAN: Code Generation via Adversarial Training;dependable Person Recognition by Means of Local Descriptors of Dynamic Facial Features;from data Disclosure to privacy Nudges: A privacy-Aware and User-Centric Personal data Management Framework;a Socio-Technical and Co-evolutionary Framework for Reducing Human-Related Risks in Cyber security and Cybercrime Ecosystems;Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling;application of Internet of things and GIS in Power Grid Emergency Command System;visualized Panoramic Display Platform for Transmission Cable Based on Space-Time Big data;Optimal Personalized DDoS Attacks Detection Strategy in Network Systems;AI and Its Risks in Android Smartphones: A Case of Google Smart Assistant;a Light-Weight Framework for Pre-submission Vetting of Android applications in App Stores;nowhere Metamorphic Malware Can Hide - A Biological Evolution Inspired Detection Scheme.

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