Secure data aggregation in IoT using Efficient-CSDA

In recent days, IoT has been widely accepted and WSN (Wireless Sensor network) is being used for variety of the applications such as transportation, medical, environmental, military, it moreover the main aim to deploy the WSN is to collect the data about the given set of phenomena. The common task of WSN is to sense the data and send over the network. Moreover, due to the various purpose such as statistical analysis, the data aggregation is required.  However, the when the dynamic network topology is considered, it is considered to be the very difficult task to provide the secure and efficient data aggregation. The main issue here is to ensure the security and accuracy of the data aggregation. Hence, in this research we have proposed an algorithm named as E-SDA (Efficient Secure Data Aggregation) in order to provide the secure data. In this, the algorithm provides the flexibility to detect the dishonest honest through neighbor monitoring. Later, extensive simulation has been done in order to prove the convergence of our algorithm

Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security

C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics.

The Medical Internet of Things (MIoT) facilitates extensive connections between cyber and physical "things" allowing for effective data fusion and remote patient diagnosis and monitoring. However, there is a risk of incorrect diagnosis when data is tampered with from the cloud or a hospital due to third-party storage services. Most of the existing systems use an owner-centric data integrity verification mechanism, which is not computationally feasible for lightweight wearable-sensor systems because of limited computing capacity and privacy leakage issues. In this regard, we design a 2-step Privacy-Preserving Multidimensional Data Stream (PPMDS) approach based on a cloudlet framework with an Uncertain Data-integrity Optimization (UDO) model and Sparse-Centric SVM (SCS) model. The UDO model enhances health data security with an adaptive cryptosystem called Cloudlet-Nonsquare Encryption Secret Transmission (C-NEST) strategy by avoiding medical disputes during data streaming based on novel signature and key generation strategies. The SCS model effectively classifies incoming queries for easy access to data by solving scalability issues. The cloudlet server measures data integrity and authentication factors to optimize third-party verification burden and computational cost. The simulation outcomes show that the proposed system optimizes average data leakage error rate by 27%, query response time and average data transmission time are reduced by 31%, and average communication-computation cost are reduced by 61% when measured against state-of-the-art approaches

A survey of state-of-the-art methods for securing medical databases

This review article presents a survey of recent work devoted to advanced state-of-the-art methods for securing of medical databases. We concentrate on three main directions, which have received attention recently: attribute-based encryption for enabling secure access to confidential medical databases distributed among several data centers; homomorphic encryption for providing answers to confidential queries in a secure manner; and privacy-preserving data mining used to analyze data stored in medical databases for verifying hypotheses and discovering trends. Only the most recent and significant work has been included

Energy efficient security and privacy management in sensor clouds

Sensor Cloud is a new model of computing for Wireless Sensor Networks, which facilitates resource sharing and enables large scale sensor networks. A multi-user distributed system, however, where resources are shared, has inherent challenges in security and privacy. The data being generated by the wireless sensors in a sensor cloud need to be protected against adversaries, which may be outsiders as well as insiders. Similarly the code which is disseminated to the sensors by the sensor cloud needs to be protected against inside and outside adversaries. Moreover, since the wireless sensors cannot support complex, energy intensive measures, the security and privacy of the data and the code have to be attained by way of lightweight algorithms. In this work, we first present two data aggregation algorithms, one based on an Elliptic Curve Cryptosystem (ECC) and the other based on symmetric key system, which provide confidentiality and integrity of data against an outside adversary and privacy against an in network adversary. A fine grained access control scheme which works on the securely aggregated data is presented next. This scheme uses Attribute Based Encryption (ABE) to achieve this objective. Finally, to securely and efficiently disseminate code in the sensor cloud, we present a code dissemination algorithm which first reduces the amount of code to be transmitted from the base station. It then uses Symmetric Proxy Re-encryption along with Bloom filters and HMACs to protect the code against eavesdropping and false code injection attacks. --Abstract, page iv

Secure privacy-preserving computing applications on cloud using homomorphic cryptography

The advancement of cloud computing technologies has provided users and business organisations with various cloud-based options to store and access information externally, across multiple platforms and geographic locations. The cloud also has the ability to deliver scalable and high-performance computing services on demand and in a cost-effective manner while helping users to avoid the trouble of maintaining large data centres and complex computing facilities. The economies of scale increase revenue for cloud providers and lower costs for cloud users. The resulting on-demand model of computing allows providers to achieve better resource utilization through statistical multiplexing, and enables users to avoid the costs of resource over-provisioning through dynamic scaling. However, there are major security and privacy concerns when data is stored in external cloud storage systems. For example, when personal information is stored in unencrypted formats on the cloud, service providers can learn many details about the users such as their preferences, past behaviours and biometric identities. The widely distributed nature of cloud architectures means that server farms can be located in many countries or geographic locations that might be under different laws and regulations regarding user privacy. Furthermore, cloud service providers may encrypt data in-transit, but not while user data is stored on their servers, causing the reluctance of many business organisations to outsource the storage of their sensitive and valuable data, which can be major targets for attacks coming from both outside attackers and insiders. Therefore, encrypting the data when it is stored on the cloud is an important task to guarantee the confidentiality and privacy of users data. However, traditional cryptographic techniques make it difficult for processing tasks such as searching, updating or checking the integrity of encrypted data without asking clients to download and decrypt large amounts of data from the cloud. To realise the full potential of cloud computing, better cryptographic schemes are required. They should enable the cloud to perform various computing operations on encrypted data and return encrypted results to customers. Another desirable feature is how a cryptographic scheme can allow different parties to combine their encrypted data and perform some computing tasks on the cloud without compromising the confidentiality and privacy of the data of each party. Recently, homomorphic cryptography has increasingly been the focus of researchers because this technology has a great potential to provide the desirable features described above. Homomorphic encryption can be implemented either as a symmetric or a public-private asymmetric key paradigm. This technique allows many types of computing operations to be performed on ciphertext and output encrypted results which, when decrypted, are found to be identical to the results of the same operations performed on plaintext data. With a homomorphic cryptosystem, many computational circuits can now be homomorphically evaluated, producing programs that might be run on encryptions of their inputs to produce an encryption of their output. Since the inputs of such programs are encrypted, a computation task can be performed on an untrusted cloud without revealing any inputs and internal states. In this thesis, we focus the design and implementation of various application models of homomorphic cryptography so that the cloud can be used more effective and securely to store and process sensitive customer data. Our research works throughout many chapters of this thesis also provide valuable information regarding the security of homomorphic cryptography in many use case scenarios. We illustrate how homomorphic cryptography can be applied effectively with all of its flexibility, power and usefulness in many applications ranging from smart grid, e-commerce to secret sharing. In this thesis, we also propose approaches to enhance the efficiency and effectiveness of homomorphic cryptography, so that these cryptographic schemes can be applied not only in current cloud-based application, but also in larger, more mission-critical applications in the future

Secure Data Aggregation in Wireless Sensor Network using Chinese Remainder Theorem

A new method of lossless Secure Data Aggregation for Wireless Sensor Network is presented. Secure Data Aggregation is achieved using the popular Chinese Remainder theorem. Here, an ‘Augmented Chinese Remainder System’ is introduced that incorporates additional features to enforce a higher level of security to the aggregated data. The scheme provides inbuilt signature verification and eliminates the need for separate data validation algorithms. The method achieves data integrity and authentication simultaneously in addition to data aggregation for the data forwarded from the Cluster Head to the Base Station. The aggregate contains the entire individual data in the encrypted form and the receiver de-aggregates it to get the original data in full. The Augmented Chinese Remainder System can be extended to secure Multi-level Data Aggregation for WSN

Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption

استرجاع الصور المستند إلى المحتوى (CBIR) هو تقنية تستخدم لاسترداد الصور من قاعدة بيانات الصور. ومع ذلك، فإن عملية CBIR تعاني من دقة أقل في استرداد الصور من قاعدة بيانات صور واسعة النطاق وضمان خصوصية الصور. تهدف هذه الورقة إلى معالجة قضايا الدقة باستخدام تقنيات التعلم العميق كطريقة CNN. أيضًا، توفير الخصوصية اللازمة للصور باستخدام طرق تشفير متماثلة تمامًا بواسطة Cheon و Kim و Kim و Song (CKKS). ولتحقيق هذه الأهداف تم اقتراح نظام RCNN_CKKS يتضمن جزأين. يستخرج الجزء الأول (المعالجة دون اتصال بالإنترنت–) لاستخراج الخصائص العالية المستوى استنادًا إلى طبقة التسطيح في شبكة عصبية تلافيفية (CNN) ثم يخزن هذه الميزات في مجموعة بيانات جديدة. في الجزء الثاني (المعالجة عبر الإنترنت) ، يرسل العميل الصورة المشفرة إلى الخادم ، والتي تعتمد على نموذج CNN المدرب لاستخراج ميزات الصورة المرسلة. بعد ذلك، تتم مقارنة الميزات المستخرجة مع الميزات المخزنة باستخدام طريقة Hamming Distance لاسترداد جميع الصور المتشابهة. أخيرًا، يقوم الخادم بتشفير جميع الصور المسترجعة وإرسالها إلى العميل. كانت نتائج التعلم العميق على الصور العادية 97.94٪ للتصنيف و98.94٪ للصور المسترجعة. في الوقت نفسه، تم استخدام اختبار NIST للتحقق من أمان CKKS عند تطبيقه على مجموعة بيانات المعهد الكندي للأبحاث المتقدمة (CIFAR-10). من خلال هذه النتائج، استنتج الباحثون أن التعلم العميق هو وسيلة فعالة لاستعادة الصور وأن طريقة CKKS مناسبة لحماية خصوصية الصورة.A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.94% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection

Identity and Aggregate Signature-Based Authentication Protocol for IoD Deployment Military Drone

With the rapid miniaturization in sensor technology, ruddervator, arduino, and multi-rotor system, drone technology has fascinated researchers in the field of network security. It is of critical significance given the advancement in modern strategic narratives. This has special relevance to drone-related operations. This technology can be controlled remotely by an invisible yet credible operator sitting to a powerful intelligence computer system (PICS) or an airborne control and command platform (AC2P). The two types of drones (reconnaissance and attacking) can communicate with each other and with the PICS or AC2P through wireless network channels referred to as Flying Ad Hoc Network or Unmanned Aerial Vehicular Network (FANET or UAVN). This mode of communication is not without some inconvenience. For instance, when the line of sight is broken, communication is mainly carried out through satellite using GPS (Global Positioning System) signals. Both GPS and UAVN/FANET use open network channels for data broadcasting, which are exposed to several threats, thus making security risky and challenging. This risk is specifically eminent in monitoring data transmission traffic, espionage, troop movement, border surveillance, searching, and warfare battlefield phenomenon, etc. This issue of security risk can be minimized conspicuously by developing a robust authentication scheme for IoD deployment military drones. Therefore, this research illustrates the designing of two separate protocols based on the aggregate signature, identity, pairing cryptography, and Computational Diffie-Hellman Problem (CDHP) to guarantee data integrity, authorization, and confidentiality among drones and AC2P/PICS. More importantly, the outdated data transmission flaw has also been tackled, which is of obvious concern to the past designed protocols. The security of the proposed designs is formally verified using a random oracle model (ROM), a real-or-random (ROR) model, and by informally using pragmatic illustration and mathematical lemmas. Nonetheless, the performance analysis section will be executed using the algorithmic big-O notation. The results show that these protocols are verifiably protected in the ROM and ROR model using the CDHP