Visible to the public Biblio

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2019-04-01
Zhang, X., Li, R., Cui, B..  2018.  A security architecture of VANET based on blockchain and mobile edge computing. 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN). :258–259.

The development of Vehicular Ad-hoc NETwork (VANET) has brought many conveniences to human beings, but also brings a very prominent security problem. The traditional solution to the security problem is based on centralized approach which requires a trusted central entity which exists a single point of failure problem. Moreover, there is no approach of technical level to ensure security of data. Therefore, this paper proposes a security architecture of VANET based on blockchain and mobile edge computing. The architecture includes three layers, namely perception layer, edge computing layer and service layer. The perception layer ensures the security of VANET data in the transmission process through the blockchain technology. The edge computing layer provides computing resources and edge cloud services to the perception layer. The service layer uses the combination of traditional cloud storage and blockchain to ensure the security of data.

2019-03-11
Li, Z., Xie, X., Ma, X., Guan, Z..  2018.  Trustworthiness Optimization of Industrial Cluster Network Platform Based on Blockchain. 2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS). :1–6.

Industrial cluster is an important organization form and carrier of development of small and medium-sized enterprises, and information service platform is an important facility of industrial cluster. Improving the credibility of the network platform is conducive to eliminate the adverse effects of distrust and information asymmetry on industrial clusters. The decentralization, transparency, openness, and intangibility of block chain technology make it an inevitable choice for trustworthiness optimization of industrial cluster network platform. This paper first studied on trusted standard of industry cluster network platform and construct a new trusted framework of industry cluster network platform. Then the paper focus on trustworthiness optimization of data layer and application layer of the platform. The purpose of this paper is to build an industrial cluster network platform with data access, information trustworthiness, function availability, high-speed and low consumption, and promote the sustainable and efficient development of industrial cluster.

2019-03-06
Cuzzocrea, A., Damiani, E..  2018.  Pedigree-Ing Your Big Data: Data-Driven Big Data Privacy in Distributed Environments. 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). :675-681.
This paper introduces a general framework for supporting data-driven privacy-preserving big data management in distributed environments, such as emerging Cloud settings. The proposed framework can be viewed as an alternative to classical approaches where the privacy of big data is ensured via security-inspired protocols that check several (protocol) layers in order to achieve the desired privacy. Unfortunately, this injects considerable computational overheads in the overall process, thus introducing relevant challenges to be considered. Our approach instead tries to recognize the "pedigree" of suitable summary data representatives computed on top of the target big data repositories, hence avoiding computational overheads due to protocol checking. We also provide a relevant realization of the framework above, the so-called Data-dRIven aggregate-PROvenance privacypreserving big Multidimensional data (DRIPROM) framework, which specifically considers multidimensional data as the case of interest.
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.

2019-03-04
Lin, Y., Qi, Z., Wu, H., Yang, Z., Zhang, J., Wenyin, L..  2018.  CoderChain: A BlockChain Community for Coders. 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN). :246–247.
An online community based on blockchain is proposed for software developers to share, assess, and learn codes and other codes or software related knowledge. It involves three modules or roles, namely: developer (or coder, or more generally, knowledge contributor), code (or knowledge contribution), and jury (or assessor, who is usually a developer with advanced skills), in addition to the blockchain based database. Each full node of the blockchain hosts a copy of all activities of developers in such community, including uploading contributions, assessing others' contributions, and conducting transactions. Smart contracts are applicable to automate transactions after code assessment or other related activities. The system aims to assess and improve the value of codes accurately, stimulate the creativity of the developers, and improve software development efficiency, so as to establish a virtuous cycle of a software development community.
2019-02-25
Xu, H., Hu, L., Liu, P., Xiao, Y., Wang, W., Dayal, J., Wang, Q., Tang, Y..  2018.  Oases: An Online Scalable Spam Detection System for Social Networks. 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). :98–105.
Web-based social networks enable new community-based opportunities for participants to engage, share their thoughts, and interact with each other. Theses related activities such as searching and advertising are threatened by spammers, content polluters, and malware disseminators. We propose a scalable spam detection system, termed Oases, for uncovering social spam in social networks using an online and scalable approach. The novelty of our design lies in two key components: (1) a decentralized DHT-based tree overlay deployment for harvesting and uncovering deceptive spam from social communities; and (2) a progressive aggregation tree for aggregating the properties of these spam posts for creating new spam classifiers to actively filter out new spam. We design and implement the prototype of Oases and discuss the design considerations of the proposed approach. Our large-scale experiments using real-world Twitter data demonstrate scalability, attractive load-balancing, and graceful efficiency in online spam detection for social networks.
2019-01-16
Uddin, M. Y. S., Venkatasubramanian, N..  2018.  Edge Caching for Enriched Notifications Delivery in Big Active Data. 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). :696–705.
In this paper, we propose a set of caching strategies for big active data (BAD) systems. BAD is a data management paradigm that allows ingestion of massive amount of data from heterogeneous sources, such as sensor data, social networks, web and crowdsourced data in a large data cluster consisting of many computing and storage nodes, and enables a very large number of end users to subscribe to those data items through declarative subscriptions. A set of distributed broker nodes connect these end users to the backend data cluster, manage their subscriptions and deliver the subscription results to the end users. Unlike the most traditional publish-subscribe systems that match subscriptions against a single stream of publications to generate notifications, BAD can match subscriptions across multiple publications (by leveraging storage in the backend) and thus can enrich notifications with a rich set of diverse contents. As the matched results are delivered to the end users through the brokers, the broker node caches the results for a while so that the subscribers can retrieve them with reduced latency. Interesting research questions arise in this context so as to determine which result objects to cache or drop when the cache becomes full (eviction-based caching) or to admit objects with an explicit expiration time indicating how much time they should reside in the cache (TTL based caching). To this end, we propose a set of caching strategies for the brokers and show that the schemes achieve varying degree of efficiency in terms of notification delivery in the BAD system. We evaluate our schemes via a prototype implementation and through detailed simulation studies.
2018-12-10
Lobato, A. G. P., Lopez, M. A., Sanz, I. J., Cárdenas, A. A., Duarte, O. C. M. B., Pujolle, G..  2018.  An Adaptive Real-Time Architecture for Zero-Day Threat Detection. 2018 IEEE International Conference on Communications (ICC). :1–6.

Attackers create new threats and constantly change their behavior to mislead security systems. In this paper, we propose an adaptive threat detection architecture that trains its detection models in real time. The major contributions of the proposed architecture are: i) gather data about zero-day attacks and attacker behavior using honeypots in the network; ii) process data in real time and achieve high processing throughput through detection schemes implemented with stream processing technology; iii) use of two real datasets to evaluate our detection schemes, the first from a major network operator in Brazil and the other created in our lab; iv) design and development of adaptive detection schemes including both online trained supervised classification schemes that update their parameters in real time and learn zero-day threats from the honeypots, and online trained unsupervised anomaly detection schemes that model legitimate user behavior and adapt to changes. The performance evaluation results show that proposed architecture maintains an excellent trade-off between threat detection and false positive rates and achieves high classification accuracy of more than 90%, even with legitimate behavior changes and zero-day threats.

Wang, Y., Ren, Z., Zhang, H., Hou, X., Xiao, Y..  2018.  “Combat Cloud-Fog” Network Architecture for Internet of Battlefield Things and Load Balancing Technology. 2018 IEEE International Conference on Smart Internet of Things (SmartIoT). :263–268.

Recently, the armed forces want to bring the Internet of Things technology to improve the effectiveness of military operations in battlefield. So the Internet of Battlefield Things (IoBT) has entered our view. And due to the high processing latency and low reliability of the “combat cloud” network for IoBT in the battlefield environment, in this paper , a novel “combat cloud-fog” network architecture for IoBT is proposed. The novel architecture adds a fog computing layer which consists of edge network equipment close to the users in the “combat-cloud” network to reduce latency and enhance reliability. Meanwhile, since the computing capability of the fog equipment are weak, it is necessary to implement distributed computing in the “combat cloud-fog” architecture. Therefore, the distributed computing load balancing problem of the fog computing layer is researched. Moreover, a distributed generalized diffusion strategy is proposed to decrease latency and enhance the stability and survivability of the “combat cloud-fog” network system. The simulation result indicates that the load balancing strategy based on generalized diffusion algorithm could decrease the task response latency and support the efficient processing of battlefield information effectively, which is suitable for the “combat cloud- fog” network architecture.

2018-11-14
Krishna, M. B., Rodrigues, J. J. P. C..  2017.  Two-Phase Incentive-Based Secure Key System for Data Management in Internet of Things. 2017 IEEE International Conference on Communications (ICC). :1–6.
Internet of Things (IoT) distributed secure data management system is characterized by authentication, privacy policies to preserve data integrity. Multi-phase security and privacy policies ensure confidentiality and trust between the users and service providers. In this regard, we present a novel Two-phase Incentive-based Secure Key (TISK) system for distributed data management in IoT. The proposed system classifies the IoT user nodes and assigns low-level, high-level security keys for data transactions. Low-level secure keys are generic light-weight keys used by the data collector nodes and data aggregator nodes for trusted transactions. TISK phase-I Generic Service Manager (GSM-C) module verifies the IoT devices based on self-trust incentive and server-trust incentive levels. High-level secure keys are dedicated special purpose keys utilized by data manager nodes and data expert nodes for authorized transactions. TISK phase-II Dedicated Service Manager (DSM-C) module verifies the certificates issued by GSM-C module. DSM-C module further issues high-level secure keys to data manager nodes and data expert nodes for specific purpose transactions. Simulation results indicate that the proposed TISK system reduces the key complexity and key cost to ensure distributed secure data management in IoT network.
2018-09-28
Song, Youngho, Shin, Young-sung, Jang, Miyoung, Chang, Jae-Woo.  2017.  Design and implementation of HDFS data encryption scheme using ARIA algorithm on Hadoop. 2017 IEEE International Conference on Big Data and Smart Computing (BigComp). :84–90.

Hadoop is developed as a distributed data processing platform for analyzing big data. Enterprises can analyze big data containing users' sensitive information by using Hadoop and utilize them for their marketing. Therefore, researches on data encryption have been widely done to protect the leakage of sensitive data stored in Hadoop. However, the existing researches support only the AES international standard data encryption algorithm. Meanwhile, the Korean government selected ARIA algorithm as a standard data encryption scheme for domestic usages. In this paper, we propose a HDFS data encryption scheme which supports both ARIA and AES algorithms on Hadoop. First, the proposed scheme provides a HDFS block-splitting component that performs ARIA/AES encryption and decryption under the Hadoop distributed computing environment. Second, the proposed scheme provides a variable-length data processing component that can perform encryption and decryption by adding dummy data, in case when the last data block does not contains 128-bit data. Finally, we show from performance analysis that our proposed scheme is efficient for various applications, such as word counting, sorting, k-Means, and hierarchical clustering.

Lu, Z., Shen, H..  2017.  A New Lower Bound of Privacy Budget for Distributed Differential Privacy. 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). :25–32.

Distributed data aggregation via summation (counting) helped us to learn the insights behind the raw data. However, such computing suffered from a high privacy risk of malicious collusion attacks. That is, the colluding adversaries infer a victim's privacy from the gaps between the aggregation outputs and their source data. Among the solutions against such collusion attacks, Distributed Differential Privacy (DDP) shows a significant effect of privacy preservation. Specifically, a DDP scheme guarantees the global differential privacy (the presence or absence of any data curator barely impacts the aggregation outputs) by ensuring local differential privacy at the end of each data curator. To guarantee an overall privacy performance of a distributed data aggregation system against malicious collusion attacks, part of the existing work on such DDP scheme aim to provide an estimated lower bound of privacy budget for the global differential privacy. However, there are two main problems: low data utility from using a large global function sensitivity; unknown privacy guarantee when the aggregation sensitivity of the whole system is less than the sum of the data curator's aggregation sensitivity. To address these problems while ensuring distributed differential privacy, we provide a new lower bound of privacy budget, which works with an unconditional aggregation sensitivity of the whole distributed system. Moreover, we study the performance of our privacy bound in different scenarios of data updates. Both theoretical and experimental evaluations show that our privacy bound offers better global privacy performance than the existing work.

2018-09-12
Nagaratna, M., Sowmya, Y..  2017.  M-sanit: Computing misusability score and effective sanitization of big data using Amazon elastic MapReduce. 2017 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC). :029–035.
The invent of distributed programming frameworks like Hadoop paved way for processing voluminous data known as big data. Due to exponential growth of data, enterprises started to exploit the availability of cloud infrastructure for storing and processing big data. Insider attacks on outsourced data causes leakage of sensitive data. Therefore, it is essential to sanitize data so as to preserve privacy or non-disclosure of sensitive data. Privacy Preserving Data Publishing (PPDP) and Privacy Preserving Data Mining (PPDM) are the areas in which data sanitization plays a vital role in preserving privacy. The existing anonymization techniques for MapReduce programming can be improved to have a misusability measure for determining the level of sanitization to be applied to big data. To overcome this limitation we proposed a framework known as M-Sanit which has mechanisms to exploit misusability score of big data prior to performing sanitization using MapReduce programming paradigm. Our empirical study using the real world cloud eco system such as Amazon Elastic Cloud Compute (EC2) and Amazon Elastic MapReduce (EMR) reveals the effectiveness of misusability score based sanitization of big data prior to publishing or mining it.
2018-05-24
Tosh, D. K., Shetty, S., Liang, X., Kamhoua, C. A., Kwiat, K. A., Njilla, L..  2017.  Security Implications of Blockchain Cloud with Analysis of Block Withholding Attack. 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). :458–467.

The blockchain technology has emerged as an attractive solution to address performance and security issues in distributed systems. Blockchain's public and distributed peer-to-peer ledger capability benefits cloud computing services which require functions such as, assured data provenance, auditing, management of digital assets, and distributed consensus. Blockchain's underlying consensus mechanism allows to build a tamper-proof environment, where transactions on any digital assets are verified by set of authentic participants or miners. With use of strong cryptographic methods, blocks of transactions are chained together to enable immutability on the records. However, achieving consensus demands computational power from the miners in exchange of handsome reward. Therefore, greedy miners always try to exploit the system by augmenting their mining power. In this paper, we first discuss blockchain's capability in providing assured data provenance in cloud and present vulnerabilities in blockchain cloud. We model the block withholding (BWH) attack in a blockchain cloud considering distinct pool reward mechanisms. BWH attack provides rogue miner ample resources in the blockchain cloud for disrupting honest miners' mining efforts, which was verified through simulations.

2018-04-02
Boicea, A., Radulescu, F., Truica, C. O., Costea, C..  2017.  Database Encryption Using Asymmetric Keys: A Case Study. 2017 21st International Conference on Control Systems and Computer Science (CSCS). :317–323.

Data security has become an issue of increasing importance, especially for Web applications and distributed databases. One solution is using cryptographic algorithms whose improvement has become a constant concern. The increasing complexity of these algorithms involves higher execution times, leading to an application performance decrease. This paper presents a comparison of execution times for three algorithms using asymmetric keys, depending on the size of the encryption/decryption keys: RSA, ElGamal, and ECIES. For this algorithms comparison, a benchmark using Java APIs and an application for testing them on a test database was created.

2018-03-19
Al-Aaridhi, R., Yueksektepe, A., Graffi, K..  2017.  Access Control for Secure Distributed Data Structures in Distributed Hash Tables. 2017 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN). :1–3.
Peer-To-Peer (P2P) networks open up great possibilities for intercommunication, collaborative and social projects like file sharing, communication protocols or social networks while offering advantages over the conventional Client-Server model of computing pattern. Such networks counter the problems of centralized servers such as that P2P networks can scale to millions without additional costs. In previous work, we presented Distributed Data Structure (DDS) which offers a middle-ware scheme for distributed applications. This scheme builds on top of DHT (Distributed Hash Table) based P2P overlays, and offers distributed data storage services as a middle-ware it still needs to address security issues. The main objective of this paper is to investigate possible ways to handle the security problem for DDS, and to develop a possibly reusable security architecture for access control for secure distributed data structures in P2P networks without depending on trusted third parties.
2018-02-28
Arellanes, D., Lau, K. K..  2017.  Exogenous Connectors for Hierarchical Service Composition. 2017 IEEE 10th Conference on Service-Oriented Computing and Applications (SOCA). :125–132.

Service composition is currently done by (hierarchical) orchestration and choreography. However, these approaches do not support explicit control flow and total compositionality, which are crucial for the scalability of service-oriented systems. In this paper, we propose exogenous connectors for service composition. These connectors support both explicit control flow and total compositionality in hierarchical service composition. To validate and evaluate our proposal, we present a case study based on the popular MusicCorp.

2018-02-21
Lyu, L., Law, Y. W., Jin, J., Palaniswami, M..  2017.  Privacy-Preserving Aggregation of Smart Metering via Transformation and Encryption. 2017 IEEE Trustcom/BigDataSE/ICESS. :472–479.

This paper proposes a novel privacy-preserving smart metering system for aggregating distributed smart meter data. It addresses two important challenges: (i) individual users wish to publish sensitive smart metering data for specific purposes, and (ii) an untrusted aggregator aims to make queries on the aggregate data. We handle these challenges using two main techniques. First, we propose Fourier Perturbation Algorithm (FPA) and Wavelet Perturbation Algorithm (WPA) which utilize Fourier/Wavelet transformation and distributed differential privacy (DDP) to provide privacy for the released statistic with provable sensitivity and error bounds. Second, we leverage an exponential ElGamal encryption mechanism to enable secure communications between the users and the untrusted aggregator. Standard differential privacy techniques perform poorly for time-series data as it results in a Θ(n) noise to answer n queries, rendering the answers practically useless if n is large. Our proposed distributed differential privacy mechanism relies on Gaussian principles to generate distributed noise, which guarantees differential privacy for each user with O(1) error, and provides computational simplicity and scalability. Compared with Gaussian Perturbation Algorithm (GPA) which adds distributed Gaussian noise to the original data, the experimental results demonstrate the superiority of the proposed FPA and WPA by adding noise to the transformed coefficients.

2018-02-15
Yonetani, R., Boddeti, V. N., Kitani, K. M., Sato, Y..  2017.  Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption. 2017 IEEE International Conference on Computer Vision (ICCV). :2059–2069.

We propose a privacy-preserving framework for learning visual classifiers by leveraging distributed private image data. This framework is designed to aggregate multiple classifiers updated locally using private data and to ensure that no private information about the data is exposed during and after its learning procedure. We utilize a homomorphic cryptosystem that can aggregate the local classifiers while they are encrypted and thus kept secret. To overcome the high computational cost of homomorphic encryption of high-dimensional classifiers, we (1) impose sparsity constraints on local classifier updates and (2) propose a novel efficient encryption scheme named doublypermuted homomorphic encryption (DPHE) which is tailored to sparse high-dimensional data. DPHE (i) decomposes sparse data into its constituent non-zero values and their corresponding support indices, (ii) applies homomorphic encryption only to the non-zero values, and (iii) employs double permutations on the support indices to make them secret. Our experimental evaluation on several public datasets shows that the proposed approach achieves comparable performance against state-of-the-art visual recognition methods while preserving privacy and significantly outperforms other privacy-preserving methods.

2018-02-06
Shi, Y., Piao, C., Zheng, L..  2017.  Differential-Privacy-Based Correlation Analysis in Railway Freight Service Applications. 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :35–39.

With the development of modern logistics industry railway freight enterprises as the main traditional logistics enterprises, the service mode is facing many problems. In the era of big data, for railway freight enterprises, coordinated development and sharing of information resources have become the requirements of the times, while how to protect the privacy of citizens has become one of the focus issues of the public. To prevent the disclosure or abuse of the citizens' privacy information, the citizens' privacy needs to be preserved in the process of information opening and sharing. However, most of the existing privacy preserving models cannot to be used to resist attacks with continuously growing background knowledge. This paper presents the method of applying differential privacy to protect associated data, which can be shared in railway freight service association information. First, the original service data need to slice by optimal shard length, then differential method and apriori algorithm is used to add Laplace noise in the Candidate sets. Thus the citizen's privacy information can be protected even if the attacker gets strong background knowledge. Last, sharing associated data to railway information resource partners. The steps and usefulness of the discussed privacy preservation method is illustrated by an example.

2018-02-02
Grewe, D., Wagner, M., Frey, H..  2017.  ICN-based open, distributed data market place for connected vehicles: Challenges and research directions. 2017 IEEE International Conference on Communications Workshops (ICC Workshops). :265–270.

Currently, the networking of everyday objects, socalled Internet of Things (IoT), such as vehicles and home automation environments is progressing rapidly. Formerly deployed as domain-specific solutions, the development is continuing to link different domains together to form a large heterogeneous IoT ecosystem. This development raises challenges in different fields such as scalability of billions of devices, interoperability across different IoT domains and the need of mobility support. The Information-Centric Networking (ICN) paradigm is a promising candidate to form a unified platform to connect different IoT domains together including infrastructure, wireless, and ad-hoc environments. This paper describes a vision of a harmonized architectural design providing dynamic access of data and services based on an ICN. Within the context of connected vehicles, the paper introduces requirements and challenges of the vision and contributes in open research directions in Information-Centric Networking.

Huang, W., Bruck, J..  2016.  Secure RAID schemes for distributed storage. 2016 IEEE International Symposium on Information Theory (ISIT). :1401–1405.

We propose secure RAID, i.e., low-complexity schemes to store information in a distributed manner that is resilient to node failures and resistant to node eavesdropping. We generalize the concept of systematic encoding to secure RAID and show that systematic schemes have significant advantages in the efficiencies of encoding, decoding and random access. For the practical high rate regime, we construct three XOR-based systematic secure RAID schemes with optimal encoding and decoding complexities, from the EVENODD codes and B codes, which are array codes widely used in the RAID architecture. These schemes optimally tolerate two node failures and two eavesdropping nodes. For more general parameters, we construct efficient systematic secure RAID schemes from Reed-Solomon codes. Our results suggest that building “keyless”, information-theoretic security into the RAID architecture is practical.

Aslan, M., Matrawy, A..  2016.  Adaptive consistency for distributed SDN controllers. 2016 17th International Telecommunications Network Strategy and Planning Symposium (Networks). :150–157.

In this paper, we introduce the use of adaptive controllers into software-defined networking (SDN) and propose the use of adaptive consistency models in the context of distributed SDN controllers. These adaptive controllers can tune their own configurations in real-time in order to enhance the performance of the applications running on top of them. We expect that the use of such controllers could alleviate some of the emerging challenges in SDN that could have an impact on the performance, security, or scalability of the network. Further, we propose extending the SDN controller architecture to support adaptive consistency based on tunable consistency models. Finally, we compare the performance of a proof-of-concept distributed load-balancing application when it runs on-top of: (1) an adaptive and (2) a non-adaptive controller. Our results indicate that adaptive controllers were more resilient to sudden changes in the network conditions than the non-adaptive ones.

2018-01-23
Acar, A., Celik, Z. B., Aksu, H., Uluagac, A. S., McDaniel, P..  2017.  Achieving Secure and Differentially Private Computations in Multiparty Settings. 2017 IEEE Symposium on Privacy-Aware Computing (PAC). :49–59.

Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (SMC) is a viable panacea for this, allowing distributed parties to make computations while the parties learn nothing about their data, but the final result. Although SMC is instrumental in such distributed settings, it does not provide any guarantees not to leak any information about individuals to adversaries. Differential privacy (DP) can be utilized to address this; however, achieving SMC with DP is not a trivial task, either. In this paper, we propose a novel Secure Multiparty Distributed Differentially Private (SM-DDP) protocol to achieve secure and private computations in a multiparty environment. Specifically, with our protocol, we simultaneously achieve SMC and DP in distributed settings focusing on linear regression on horizontally distributed data. That is, parties do not see each others’ data and further, can not infer information about individuals from the final constructed statistical model. Any statistical model function that allows independent calculation of local statistics can be computed through our protocol. The protocol implements homomorphic encryption for SMC and functional mechanism for DP to achieve the desired security and privacy guarantees. In this work, we first introduce the theoretical foundation for the SM-DDP protocol and then evaluate its efficacy and performance on two different datasets. Our results show that one can achieve individual-level privacy through the proposed protocol with distributed DP, which is independently applied by each party in a distributed fashion. Moreover, our results also show that the SM-DDP protocol incurs minimal computational overhead, is scalable, and provides security and privacy guarantees.

2018-01-16
Richardson, D. P., Lin, A. C., Pecarina, J. M..  2017.  Hosting distributed databases on internet of things-scale devices. 2017 IEEE Conference on Dependable and Secure Computing. :352–357.

The Internet of Things (IoT) era envisions billions of interconnected devices capable of providing new interactions between the physical and digital worlds, offering new range of content and services. At the fundamental level, IoT nodes are physical devices that exist in the real world, consisting of networking, sensor, and processing components. Some application examples include mobile and pervasive computing or sensor nets, and require distributed device deployment that feed information into databases for exploitation. While the data can be centralized, there are advantages, such as system resiliency and security to adopting a decentralized architecture that pushes the computation and storage to the network edge and onto IoT devices. However, these devices tend to be much more limited in computation power than traditional racked servers. This research explores using the Cassandra distributed database on IoT-representative device specifications. Experiments conducted on both virtual machines and Raspberry Pi's to simulate IoT devices, examined latency issues with network compression, processing workloads, and various memory and node configurations in laboratory settings. We demonstrate that distributed databases are feasible on Raspberry Pi's as IoT representative devices and show findings that may help in application design.