Now showing items 1-20 of 565

    • Use of artificial intelligence to improve resilience and preparedness against adverse flood events

      Saravi, Sara; Kalawsky, Roy; Joannou, Demetrios; Rivas Casado, Monica; Fu, Guangtao; Meng, Fanlin; Loughborough University (MDPI AG, 2019-05-09)
      The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilience.
    • A model-based engineering methodology and architecture for resilience in systems-of-systems: a case of water supply resilience to flooding

      Joannou, Demetrios; Kalawsky, Roy; Saravi, Sara; Rivas Casado, Monica; Fu, Guangtao; Meng, Fanlin; Loughborough University (MDPI AG, 2019-03-08)
      There is a clear and evident requirement for a conscious effort to be made towards a resilient water system-of-systems (SoS) within the UK, in terms of both supply and flooding. The impact of flooding goes beyond the immediately obvious socio-aspects of disruption, cascading and affecting a wide range of connected systems. The issues caused by flooding need to be treated in a fashion which adopts an SoS approach to evaluate the risks associated with interconnected systems and to assess resilience against flooding from various perspectives. Changes in climate result in deviations in frequency and intensity of precipitation; variations in annual patterns make planning and management for resilience more challenging. This article presents a verified model-based system engineering methodology for decision-makers in the water sector to holistically, and systematically implement resilience within the water context, specifically focusing on effects of flooding on water supply. A novel resilience viewpoint has been created which is solely focused on the resilience aspects of architecture that is presented within this paper. Systems architecture modelling forms the basis of the methodology and includes an innovative resilience viewpoint to help evaluate current SoS resilience, and to design for future resilient states. Architecting for resilience, and subsequently simulating designs, is seen as the solution to successfully ensuring system performance does not suffer, and systems continue to function at the desired levels of operability. The case study presented within this paper demonstrates the application of the SoS resilience methodology on water supply networks in times of flooding, highlighting how such a methodology can be used for approaching resilience in the water sector from an SoS perspective. The methodology highlights where resilience improvements are necessary and also provides a process where architecture solutions can be proposed and tested
    • Integration and evaluation of QUIC and TCP-BBR in longhaul science data transfers

      Lopes, Raul H. C.; Franqueira, Virginia N. L.; Duncan, Rand; Jisc, Lumen House; University of Derby, College of Engineering and Technology; Brunel University London, College of Engineering, Design and Physical Sciences (EDP Sciences, 2019-09-17)
      Two recent and promising additions to the internet protocols are TCP-BBR and QUIC. BBR defines a congestion policy that promises a better control in TCP bottlenecks on long haul transfers and can also be used in the QUIC protocol. TCP-BBR is implemented in the Linux kernels above 4.9. It has been shown, however, to demand careful fine tuning in the interaction, for example, with the Linux Fair Queue. QUIC, on the other hand, replaces HTTP and TLS with a protocol on the top of UDP and thin layer to serve HTTP. It has been reported to account today for 7% of Google’s traffic. It has not been used in server-to-server transfers even if its creators see that as a real possibility. Our work evaluates the applicability and tuning of TCP-BBR and QUIC for data science transfers. We describe the deployment and performance evaluation of TCP-BBR and comparison with CUBIC and H-TCP in transfers through the TEIN link to Singaren (Singapore). Also described is the deployment and initial evaluation of a QUIC server. We argue that QUIC might be a perfect match in security and connectivity to base services that are today performed by the Xroot redirectors.
    • Privacy verification of photoDNA based on machine learning

      Nadeem, Muhammad Shahroz; Franqueira, Virginia N. L.; Zhai, Xiaojun; University of Derby, College of Engineering and Technology; University of Essex, School of Computer Science and Electronic Engineering (The Institution of Engineering and Technology (IET), 2019-10-09)
      PhotoDNA is a perceptual fuzzy hash technology designed and developed by Microsoft. It is deployed by all major big data service providers to detect Indecent Images of Children (IIOC). Protecting the privacy of individuals is of paramount importance in such images. Microsoft claims that a PhotoDNA hash cannot be reverse engineered into the original image; therefore, it is not possible to identify individuals or objects depicted in the image. In this chapter, we evaluate the privacy protection capability of PhotoDNA by testing it against machine learning. Specifically, our aim is to detect the presence of any structural information that might be utilized to compromise the privacy of the individuals via classification. Due to the widespread usage of PhotoDNA as a deterrent to IIOC by big data companies, ensuring its ability to protect privacy would be crucial. In our experimentation, we achieved a classification accuracy of 57.20%.This result indicates that PhotoDNA is resistant to machine-learning-based classification attacks.
    • First observation of an attractive interaction between a proton and a cascade baryon

      Acharya, S.; Adamová, D.; Adhya, S. P.; Adler, A.; Adolfsson, J.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; et al. (American Physical Society (APS), 2019-09-13)
      This Letter presents the first experimental observation of the attractive strong interaction between a proton and a multistrange baryon (hyperon) Ξ−. The result is extracted from two-particle correlations of combined p−Ξ−⊕¯p−¯Ξ+ pairs measured in p−Pb collisions at √sNN=5.02 TeV at the LHC with ALICE. The measured correlation function is compared with the prediction obtained assuming only an attractive Coulomb interaction and a standard deviation in the range [3.6, 5.3] is found. Since the measured p−Ξ−⊕¯p−¯Ξ+ correlation is significantly enhanced with respect to the Coulomb prediction, the presence of an additional, strong, attractive interaction is evident. The data are compatible with recent lattice calculations by the HAL-QCD Collaboration, with a standard deviation in the range [1.8, 3.7]. The lattice potential predicts a shallow repulsive Ξ− interaction within pure neutron matter and this implies stiffer equations of state for neutron-rich matter including hyperons. Implications of the strong interaction for the modeling of neutron stars are discussed.
    • GORTS: genetic algorithm based on one-by-one revision of two sides for dynamic travelling salesman problems

      Xu, Xiaolong; Yuan, Hao; Matthew, Peter; Ray, Jeffrey; Bagdasar, Ovidiu; Trovati, Marcello; University of Derby; Nanjing University of Posts and Telecommunications, Nanjing, China; Edge Hill University, Ormskirk, UK (Springer, 2019-09-21)
      The dynamic travelling salesman problem (DTSP) is a natural extension of the standard travelling salesman problem, and it has attracted significant interest in recent years due to is practical applications. In this article, we propose an efficient solution for DTSP, based on a genetic algorithm (GA), and on the one-by-one revision of two sides (GORTS). More specifically, GORTS combines the global search ability of GA with the fast convergence feature of the method of one-by-one revision of two sides, in order to find the optimal solution in a short time. An experimental platform was designed to evaluate the performance of GORTS with TSPLIB. The experimental results show that the efficiency of GORTS compares favourably against other popular heuristic algorithms for DTSP. In particular, a prototype logistics system based on GORTS for a supermarket with an online map was designed and implemented. It was shown that this can provide optimised goods distribution routes for delivery staff, while considering real-time traffic information.
    • Temporal patterns: Smart-type reasoning and applications

      Chuckravanen, Dineshen; Daykin, Jacqueline; Hunsdale, Karen; Seeam, Amar; Aberythwyth University (Mauritius Branch Campus) (International Academy, Research, and Industry Association (IARIA), 2017-02)
      Allen’s interval algebra is a calculus for temporal reasoning that was introduced in 1983. Reasoning with quali- tative time in Allen’s full interval algebra is nondeterministic polynomial time (NP) complete. Research since 1995 identified maximal tractable subclasses of this algebra via exhaustive computer search and also other ad-hoc methods. In 2003, the full classification of complexity for satisfiability problems over con- straints in Allen’s interval algebra was established algebraically. Recent research proposed scheduling based on the Fishburn- Shepp correlation inequality for posets. We describe here three potential temporal-related application areas as candidates for scheduling using this inequality
    • Allen’s interval algebra and smart-type environments

      Chuckravanen, Dineshen; Daykin, Jacqueline; Hunsdale, Karen; Seeam, Amar; Aberystwyth University (Mauritius Campus) (IARIA, 2017-03)
      Allen’s interval algebra is a calculus for temporal reasoning that was introduced in 1983. Reasoning with qualitative time in Allen’s full interval algebra is nondeterministic polynomial time (NP) complete. Research since 1995 identified maximal tractable subclasses of this algebra via exhaustive computer search and also other ad-hoc methods. In 2003, the full classification of complexity for satisfiability problems over constraints in Allen’s interval algebra was established algebraically. Recent research proposed scheduling based on the Fishburn-Shepp correlation inequality for posets. This article first reviews Allen’s calculus and surrounding computational issues in temporal reasoning. We then go on to describe three potential temporal-related application areas as candidates for scheduling using the Fishburn-Shepp inequality. We also illustrate through concrete examples, and conclude the importance of Fishburn-Shepp inequality for the suggested application areas that are the development of smart homes, intelligent conversational agents and in physiology with emphasis during time-trial physical exercise. The Fishburn-Shepp inequality will enable the development of smart type devices, which will in turn help us to have a better standard of living.
    • Towards a trusted unmanned aerial system using blockchain (BUAS) for the protection of critical infrastructure

      Barka, Ezedin; Kerrache, Chaker Abdelaziz; Benkraouda, Hadjer; Shuaib, Khaled; Ahmad, Farhan; Kurugollu, Fatih; College of Information Technology, United Arab Emirates University; Department of Mathematics and Computer Science, University of Ghardaia, Algeria; Cyber Security Research Group, University of Derby, UK (Wiley, 2019-07-29)
      With the exponential growth in the number of vital infrastructures such as nuclear plants and transport and distribution networks, these systems have become more susceptible to coordinated cyber attacks. One of the effective approaches used to strengthen the security of these infrastructures is the use of Unmanned Aerial Vehicles (UAVs) for surveillance and data collection. However, UAVs themselves are prone to attacks on their collected sensor data. Recently, Blockchain (BC) has been proposed as a revolutionary technology which can be integrated within IoT to provide a desired level of security and privacy. However, the integration of BC within IoT networks, where UAV's sensors constitute a major component, is extremely challenging. The major contribution of this study is two-fold. (1) survey of the security issues for UAV's collected sensor data, define the security requirements for such systems, and identify ways to address them. (2) propose a novel Blockchain-based solution to ensure the security of, and the trust between the UAVs and their relevant ground control stations (GCS). Our implementation results and analysis show that using UAVs as means for protecting critical infrastructure is greatly enhanced through the utilization of trusted Blockchain-based Unmanned Aerial Systems (UASs).
    • Multiclass disease predictions based on integrated clinical and genomics datasets

      Anjum, Ashiq; Subhani, Moeez; University of Derby (IARIA, 2019-06-02)
      Clinical predictions using clinical data by computational methods are common in bioinformatics. However, clinical predictions using information from genomics datasets as well is not a frequently observed phenomenon in research. Precision medicine research requires information from all available datasets to provide intelligent clinical solutions. In this paper, we have attempted to create a prediction model which uses information from both clinical and genomics datasets. We have demonstrated multiclass disease predictions based on combined clinical and genomics datasets using machine learning methods. We have created an integrated dataset, using a clinical (ClinVar) and a genomics (gene expression) dataset, and trained it using instancebased learner to predict clinical diseases. We have used an innovative but simple way for multiclass classification, where the number of output classes is as high as 75. We have used Principal Component Analysis for feature selection. The classifier predicted diseases with 73% accuracy on the integrated dataset. The results were consistent and competent when compared with other classification models. The results show that genomics information can be reliably included in datasets for clinical predictions and it can prove to be valuable in clinical diagnostics and precision medicine.
    • Realization of blockchain in named data networking-based internet-of-vehicles

      Ahmad, Farhan; Kerrache, Chaker Abdelaziz; Kurugollu, Fatih; Hussain, Rasheed; Cyber Security Research Group, University of Derby, UK; Department of Mathematics and Computer Science, University of Ghardaia, Algeria; Institute of Information Systems, Innopolis University, Russia (IEEE, 2019-07-15)
      The revolution of Internet-of-vehicles (IoV) has stimulated a substantial response from academia, research and industry due to its massive potential to improve overall transportation. Current IoV faces huge challenges due to its reliance on the IP-based network architecture. Therefore, Named Data Networking (NDN) is proposed as a promising architecture to solve issues posed by IP-based systems. Recently, Blockchains (BCs) are utilized within IoV to increase network security. However, the integration of BC within NDN-enabled IoV is still an open research problem. In this study, we proposed a novel tier-based architecture known as “Blockchain in NDN-enabled Internet-of-Vehicles (BINDN)” which can support BC within NDN-enabled IoV. BINDN can be used as a reference architecture to design security solutions in NDN-enabled IoV using BC. Further, it provides an extensive set of applications including IoV security, trust management and privacy enhancements. Moreover, we highlighted major challenges and issues when integrating BC within NDN-enabled IoV.
    • High-performance time-series quantitative retrieval from satellite images on a GPU cluster

      Xue, Yong; Liu, Jia; Ren, Kaijun; Song, Junqiang; Windmill, Christopher; Merritt, Patrick; University of Derby (IEEE, 2019-07-12)
      The quality and accuracy of remote sensing instruments continue to increase, allowing geoscientists to perform various quantitative retrieval applications to observe the geophysical variables of land, atmosphere, ocean, etc. The explosive growth of time-series remote sensing (RS) data over large-scales poses great challenges on managing, processing, and interpreting RS ‘‘Big Data.’’ To explore these time-series RS data efficiently, in this paper, we design and implement a high-performance framework to address the time-consuming time-series quantitative retrieval issue on a graphics processing unit cluster, taking the aerosol optical depth (AOD) retrieval from satellite images as a study case. The presented framework exploits the multilevel parallelism for time-series quantitative RS retrieval to promote efficiency. At the coarse-grained level of parallelism, the AOD time-series retrieval is represented as multidirected acyclic graph workflows and scheduled based on a list-based heuristic algorithm, heterogeneous earliest finish time, taking the idle slot and priorities of retrieval jobs into account. At the fine-grained level, the parallel strategies for the major remote sensing image processing algorithms divided into three categories, i.e., the point or pixel-based operations, the local operations, and the global or irregular operations have been summarized. The parallel framework was implemented with message passing interface and compute unified device architecture, and experimental results with the AOD retrieval case verify the effectiveness of the presented framework.
    • Improved Kalman filter based differentially private streaming data release in cognitive computing.

      Wang, Jun; Luo, Jing; Liu, Xiaozhu; Li, Yongkai; Liu, Shubo; Zhu, Rongbo; Anjum, Ashiq; University of Derby; South-Central University for Nationalities; Wuhan University of Technology; et al. (Elsevier, 2019-04-04)
      Cognitive computing works well based on volumes of data, which offers the guarantee of unlocking novel insights and data-driven decisions. Steaming data is a major component of aggregated data, and sharing these real-time aggregated statistics has gained a lot of benefits in decision analysis, such as traffic heat map and disease outbreaks. However, original streaming data sharing will bring users the risk of privacy disclosure. In this paper, differential privacy technology is introduced into cognitive system, and an improved Kalman filter based differentially private streaming data release scheme is proposed for privacy requirement of cognitive computing system. The feasibility of the proposed scheme has been demonstrated through analysis of the utility of sanitized data from four real datasets, and the experimental results show that the proposed scheme outperforms the Kalman filter-based method at the same level of privacy preserving.
    • Intelligent augmented keyword search on spatial entities in real-life internet of vehicles

      Li, Yanhong; Wang, Meng; Du, Xiaokun; Feng, Yuhe; Luo, Changyin; Tian, Shasha; Anjum, Ashiq; Zhu, Rongbo; University of Derby (Elsevier, 2018-12-28)
      Internet of Vehicles (IoV) has attracted wide attention from both academia and industry. Due to the popularity of the geographical devices deployed on the vehicles, a tremendous amount of spatial entities which include spatial information, unstructured information and structured information, are generated every second. This development calls for intelligent augmented spatial keyword queries (ASKQ), which intelligently takes into account the locations, unstructured information (in the form of keyword sets), structured information (in the form of boolean expressions) of 182MinzuAvespatial entities. In this paper, we take the first step to address the issue of processing ASKQ in real traffic networks of IoV environments (ASKQIV) and focus on Top-k ASKQIV queries. To support network distance pruning, keyword pruning, and boolean expression pruning intelligently and simultaneously, a novel hybrid index structure called ASKTI is proposed. Note in the real-life traffic networks of IoV environments, travel cost is not only decided by the network distance, but also decided by some additional travel factors. By considering these additional factors, a combined factor Cftc of each road (edge) in the traffic network of IoV environments is calculated, and weighted network distance is calculated and adopted. Based on ASKTI, an efficient algorithm for Top-k ASKQIV query processing is proposed. Our method can also be extended to handle boolean range ASKQIV Queries and ranking ASKQIV Queries. Finally, simulation experiments on one real traffic network of IoV environments and two synthetic spatial entity sets are conducted. The results show that our ASKTI based method is superior to its competitors.
    • Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis

      Anjum, Ashiq; University of Derby (Elsevier, 2019-03-28)
      In biomedical domain, abbreviations are appearing more and more frequently in various data sets, which has caused significant obstacles to biomedical big data analysis. The dictionary-based approach has been adopted to process abbreviations, but it cannot handle ad hoc abbreviations, and it is impossible to cover all abbreviations. To overcome these drawbacks, this paper proposes an automatic abbreviation expansion method called LMAAE (Language Model-based Automatic Abbreviation Expansion). In this method, the abbreviation is firstly divided into blocks; then, expansion candidates are generated by restoring each block; and finally, the expansion candidates are filtered and clustered to acquire the final expansion result according to the language model and clustering method. Through restrict the abbreviation to prefix abbreviation, the search space of expansion is reduced sharply. And then, the search space is continuous reduced by restrained the effective and the length of the partition. In order to validate the effective of the method, two types of experiments are designed. For standard abbreviations, the expansion results include most of the expansion in dictionary. Therefore, it has a high precision. For ad hoc abbreviations, the precisions of schema matching, knowledge fusion are increased by using this method to handle the abbreviations. Although the recall for standard abbreviation needs to be improved, but this does not affect the good complement effect for the dictionary method.
    • A survey of deep learning solutions for multimedia visual content analysis.

      Nadeem, Muhammad Shahroz; Franqueira, Virginia N. L.; Zhai, Xiaojun; Kurugollu, Fatih; University of Derby; University of Essex (IEEE, 2019-06-24)
      The increasing use of social media networks on handheld devices, especially smartphones with powerful built-in cameras, and the widespread availability of fast and high bandwidth broadband connections, added to the popularity of cloud storage, is enabling the generation and distribution of massive volumes of digital media, including images and videos. Such media is full of visual information and holds immense value in today’s world. The volume of data involved calls for automated visual content analysis systems able to meet the demands of practice in terms of efficiency and effectiveness. Deep Learning (DL) has recently emerged as a prominent technique for visual content analysis. It is data-driven in nature and provides automatic end-to-end learning solutions without the need to rely explicitly on predefined handcrafted feature extractors. Another appealing characteristic of DL solutions is the performance they can achieve, once the network is trained, under practical constraints. This paper identifies eight problem domains which require analysis of visual artefacts in multimedia. It surveys the recent, authoritative, and best performing DL solutions and lists the datasets used in the development of these deep methods for the identified types of visual analysis problems. The paper also discusses the challenges that DL solutions face which can compromise their reliability, robustness, and accuracy for visual content analysis.
    • An SDN-enhanced load balancing technique in the cloud system.

      Kang, Byungseok; Choo, Hyunseung; Sungkyunkwan University (2016-12-07)
      The vast majority of Web services and sites are hosted in various kinds of cloud services, and ordering some level of quality of service (QoS) in such systems requires effective load-balancing policies that choose among multiple clouds. Recently, software-defined networking (SDN) is one of the most promising solutions for load balancing in cloud data center. SDN is characterized by its two distinguished features, including decoupling the control plane from the data plane and providing programmability for network application development. By using these technologies, SDN and cloud computing can improve cloud reliability, manageability, scalability and controllability. SDN-based cloud is a new type cloud in which SDN technology is used to acquire control on network infrastructure and to provide networking-as-a-service (NaaS) in cloud computing environments. In this paper, we introduce an SDN-enhanced Inter cloud Manager (S-ICM) that allocates network flows in the cloud environment. S-ICM consists of two main parts, monitoring and decision making. For monitoring, S-ICM uses SDN control message that observes and collects data, and decision-making is based on the measured network delay of packets. Measurements are used to compare S-ICM with a round robin (RR) allocation of jobs between clouds which spreads the workload equitably, and with a honeybee foraging algorithm (HFA). We see that S-ICM is better at avoiding system saturation than HFA and RR under heavy load formula using RR job scheduler. Measurements are also used to evaluate whether a simple queueing formula can be used to predict system performance for several clouds being operated under an RR scheduling policy, and show the validity of the theoretical approximation.
    • Machine-learning-based side-channel evaluation of elliptic-curve cryptographic FPGA processor.

      Mukhtar, Naila; Mehrabi, Mohamad; Kong, Yinan; Anjum, Ashiq; University of Derby; Macquarie University (MDPI, 2018-12-25)
      Security of embedded systems is the need of the hour. A mathematically secure algorithm runs on a cryptographic chip on these systems, but secret private data can be at risk due to side-channel leakage information. This research focuses on retrieving secret-key information, by performing machine-learning-based analysis on leaked power-consumption signals, from Field Programmable Gate Array (FPGA) implementation of the elliptic-curve algorithm captured from a Kintex-7 FPGA chip while the elliptic-curve cryptography (ECC) algorithm is running on it. This paper formalizes the methodology for preparing an input dataset for further analysis using machine-learning-based techniques to classify the secret-key bits. Research results reveal how pre-processing filters improve the classification accuracy in certain cases, and show how various signal properties can provide accurate secret classification with a smaller feature dataset. The results further show the parameter tuning and the amount of time required for building the machine-learning models
    • PriVeto: a fully private two round veto protocol.

      Samiran, Bag; Muhammad Ajmal, Azad; Feng, Hao; Warwick University; Derby University (Institution of Engineering and Technology, 2018-12-04)
      Veto is a prerogative to unilaterally overrule a decision. A private veto protocol consists of a number of participants who wish to decide whether or not to veto a particular motion without revealing the individual opinions. Essentially all participants jointly perform a multi-party computation (MPC) on a boolean-OR function where an input of "1" represents veto and "0" represents not veto. In 2006, Hao and Zieli´ nski presented a two round veto protocol named Anonymous Veto network (AV-net), which is exceptionally efficient in terms of the number of rounds, computation and bandwidth usage. However, AV-net has two generic issues: 1) a participant who has submitted a veto can find out whether she is the only one who vetoed; 2) the last participant who submits her input can pre-compute the boolean-OR result before submission, and may amend her input based on that knowledge. These two issues generally apply to any multi-round veto protocol where participants commit their input in the last round. In this paper, we propose a novel solution to address both issues within two rounds, which are the best possible round efficiency for a veto protocol. Our new private veto protocol, called PriVeto, has similar system complexities to AV-net, but it binds participants to their inputs in the very first round, eliminating the possibility of runtime changes to any of the inputs. At the end of the protocol, participants are strictly limited to learning nothing more than the output of the boolean-OR function and their own inputs.
    • M2M-REP: Reputation system for machines in the internet of things.

      Azad, Muhammad Ajmal; Bag, Samiran; Hao, Feng; Salah, Khaled; Newcastle University; Khalifa University (2018-08-14)
      In the age of IoT (Internet of Things), Machine-to-Machine (M2M) communication has gained significant popularity over the last few years. M2M communication systems may have a large number of autonomous connected devices that provide services without human involvement. Interacting with compromised, infected and malicious machines can bring damaging consequences in the form of network outage, machine failure, data integrity, and financial loss. Hence, users first need to evaluate the trustworthiness of machines prior to interacting with them. This can be realized by using a reputation system, which evaluates the trustworthiness of machines by utilizing the feedback collected from the users of the machines. The design of a reliable reputation system for the distributed M2M communication network should preserve user privacy and have low computation and communication overheads. To address these challenges, we propose an M2M-REP System (Machine to Machine REPutation), a privacy-preserving reputation system for evaluating the trustworthiness of autonomous machines in the M2M network. The system computes global reputation scores of machines while maintaining privacy of the individual participant score by using secure multi-party computation techniques. The M2M-REP system ensures correctness, security and privacy properties under the malicious adversarial model, and allows public verifiability without relying on a centralized trusted system. We implement a prototype of our system and evaluate the system performance in terms of the computation and bandwidth overhead.