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The volume of data is increasing rapidly currently, which poses a great challenge for resource-constrained clients to process and analyze. A promising approach for solving computation-intensive tasks is to outsource them to the cloud to take advantage of the clouds powerful computing ability. However, it also brings privacy and security issues since the data uploaded to the cloud may contain sensitive and private information which should be protected. In this paper, we address this problem and focus on the privacy-preserving and secure outsourcing of large-scale nonlinear programming problems (NLPs) subject to both linear constraints and nonlinear constraints. Large-scale NLPs play an important role in the eld of data analytics but have not received enough attention. In our outsourcing protocol, we rst apply a secure and efcient transformation scheme at the client side to encrypt its private information. Then, we use the reduced gradient method and generalized gradient method at the server side to solve the transformed large-scale NLPs under linear constraints and nonlinear constraints, respectively. We provide security analysis of the proposed protocol, and evaluate its performance. The experimental results show that we can efciently solve the large-scale NLPs and save huge amount of time for the clients.

IoT application development usually involves separate programming at the device side and server side. While separate programming style is sufcient for many simple applications, it is not suitable for many complex applications that involve complex interactions and intensive data processing. We propose EdgeProg, an edge-centric programming approach to simplify IoT application programming, motivated by the increasing popularity of edge computing. With EdgeProg, users could write application logic in a centralized manner with an augmented If-This-Then-That (IFTTT) syntax and virtual sensor mechanism. The program can be processed at the edge server, which can automatically generate the actual application code and intelligently partition the code into device code and server code, for achieving the optimal latency. EdgeProg employs dynamic linking and loading to deploy the device code on a variety of IoT devices, which do not run any application-specic codes at the start. Results show that EdgeProg achieves an average reduction of 20.96%, 27.8% and 79.41% in terms of execution latency, energy consumption, and lines of code compared with state-of-the-art approaches.

Federated learning addresses the issue of machine learning realized under constraints of privacy and security. While there have been intensive studies on building and analyzing federated regression models, this topic has not been analyzed so far in the area of fuzzy systems. To narrow down this gap, in this study, we formulate and solve a problem of unsupervised federated learning by designing an original federated FCM (F-FCM) clustering which could serve as a basis towards building a spectrum of fuzzy set constructs including rule-based models. Following a general client-server structure where the local data residing with each client are not available globally and cannot be centralized (as commonly encountered in learning scenarios), the aim is to discover an overall structure across all data. The federated gradient-based optimization realized in the horizontal mode is developed. An overall learning process is derived, which is composed of communicating gradients coming from clients and providing updates of the prototypes at the server side and passing them on to the clients. It is also shown that the relevance of the globally constructed structure is conveniently assessed in terms of granular footprints of the prototypes constructed by the F-FCM. Some illustrative examples are covered to illustrate the efficiency of the developed federated algorithm.

Federated learning (FL) unleashes the full potential of training a global statistical model collaboratively from edge clients. In wireless FL, for the scarcity of spectrum, only a fraction of clients are capable to participate in the FL training in each round. On the other hand, the performance of FL suffers from the label noise, which naturally exists in the dataset of each client. To alleviate the label noise issue, prior works proposed several client selection algorithms, where the privacy of raw data might be compromised when extra information exchange was introduced. To avoid extra information exchange in FL, we propose to leverage an algorithm that measures the noise ratio of each client based on a clean validation dataset. We then propose an online client selection framework, supported by Copeland score and multi-arm bandits, which can select the client set with low noise ratios efficiently. The proposed algorithm is performed in the server side, thereby it can be implemented in the basic FL framework seamlessly. Simulation results demonstrate that our proposed algorithm spends less training time while guaranteeing the required accuracy compared to other baseline algorithms.

The mobile fog-based cloud (MFBC) plays a major role for the Internet of Things (IoT) and is highly suitable for obtaining virtualized data without additional waiting time. However, the limited battery power of mobile devices limits access to abundant data. To address this problem, an energy-efficient and secure algorithm should be introduced to reduce energy consumption for the MFBC. In this paper, an energy-efficient and secure hybrid (EESH) algorithm is introduced for the MFBC to support the IoT. The EESH algorithm uses the voltage scaling factor to reduce energy consumption. The performance of the EESH algorithm is substantially better than the state-of-the-art contending algorithms when the number of IoT-tasks increases, and EESH consumes minimal energy. Furthermore, the identity of mobile cloud users is of paramount significance; therefore, the EESH is further secured by applying a malicious data detection (MDD) algorithm using blockchain technology. The capability of the processors on the server side of the MFBC is analyzed and improved for the IoT. The proposed EESH, MDD and underlying algorithms are programmed on the Java platform. Finally, the performance of the proposed algorithms on the MFBC is compared with that of known algorithms in terms of security, energy efficiency, throughput and latency.

Content-based Image Retrieval (CBIR) techniques have been extensively studied with the rapid growth of digital images. Generally, CBIR service is quite expensive in computational and storage resources. Thus, it is a good choice to outsource CBIR service to the cloud server that is equipped with enormous recourses. However, the privacy protection becomes a big problem, as the cloud server cannot be fully trusted. In this paper, we propose an outsourced CBIR scheme based on a novel bag-of-encrypted-words (BOEW) model. The image is encrypted by color value substitution, block permutation, and intra-block pixel permutation. Then, the local histograms are calculated from the encrypted image blocks by the cloud server. All the local histograms are clustered together, and the cluster centers are used as the encrypted visual words. In this way, the bag-of-encrypted-words (BOEW) model is built to represent each image by a feature vector, i.e., a normalized histogram of the encrypted visual words. The similarity between images can be directly measured by the Manhattan distance between feature vectors on the cloud server side. Experimental results and security analysis on the proposed scheme demonstrate its search accuracy and security.

Existing synchronous network search data extraction techniques suffer from load unbalance in the face of increasing number of concurrent users. For this reason, this paper presents the research on the optimum synchronous network search data extraction based on swarm intelligence algorithm. A traffic balancing model is constructed to determine the node balance state according to the traffic variation and influence factors of network nodes. Under the condition that the node state is known, the swarm intelligence algorithm is used to cluster the data to be synchronized and adjust the node state so that it is kept stable throughout the synchronization process. The clustered data act as the target to connect the user with the server side to achieve the optimum network search data extraction and synchronization. The experimental results show that when the number of concurrent network users is increasing, the designed technique features stable load balancing, and achieves optimum data extraction performance and low execution cost when the task completion time is less than 0.5 s.

The sheer volume of industrial Internet of Things (IIoT) malware is one of the most serious security threats in today's interconnected world, with new types of advanced persistent threats and advanced forms of obfuscations. This article presents a robust federated learning based architecture called Fed-IIoT for detecting Android malware applications in IIoT. Fed-IIoT consists of two parts: first, participant side, where the data are triggered by two dynamic poisoning attacks based on a generative adversarial network (GAN) and federated GAN; and second, server side, which aims to monitor the global model and shape a robust collaboration training model, by avoiding anomaly in aggregation by a GAN network (A3GAN) and adjust two GAN-based countermeasure algorithms. One of the main advantages of Fed-IIoT is that devices can safely participate in the IIoT and efficiently communicate with each other, with no privacy issues. We evaluate our solutions through experiments on various features using three IoT datasets. The results confirm the high accuracy rates of our attack and defense algorithms and show that the A3GAN defensive approach preserves the robustness of data privacy for Android mobile users and is about 8% higher accuracy with existing state-of-the-art solutions.

The Internet of Things (IoT) is a framework of various services and smart technologies that mutually communicate information between mobile devices and users or just between devices with the help of Internet connectivity. The dramatic progression of IoT helps numerous network applications and communication technologies to introduce state-of-the-art communication models for enabling interaction among mobile server, clients, and various other smart entities. Now-a-days, online mobile services have gained huge attention by providing ample convenience to the distant users. However, it is necessary to secure the information, being exchanged among mobile clients and server. Therefore, a large number of authentication protocols have been presented but majority of them are unsuitable to fulfill novel security requirements and standards. Moreover, they are incompatible for the IoT environment due to higher computation and communication complexity. Consequently, there is a dire need of developing an adequate, reliable, and cost-effective authentication protocol. In this article, we introduce a novel identity-based key agreement protocol using the puncturable pseudorandom functions for mobile clients in the IoT environment. The proposed PSK-MC protocol enables two mobile clients to accomplish mutual authentication via server. The proposed protocol is evaluated formally and informally to determine its security strength. The formal security analysis is presented using the widely used random oracle model. Moreover, all the cryptographic operations used at mobile client side are executed on a mobile device, while the operations used at the server side are implemented on a desktop machine to get the experimental results to determine computation cost. The performance analysis reveals the fact that our protocol is comparatively better than related protocols by exhibiting least communication and computation overhead.

This paper first designed the overall scheme of a wireless-based intelligent environment monitoring system; this system is comprised of the following four parts: server part, client part, slave computer part, and master computer part. The master computer is equipped with a wireless transmission module and an LCD module. Slave computers are equipped with temperature-humidity sensing modules and wireless transmission modules. The server side has two major interfaces: data display and server establishment. An Android phone as the client can realize the communication between the mobile APP and server through network communication to obtain environmental data in real time. The hardware part of the system consists of wireless transmission module, temperature and humidity module, smoke module, lighting module, flame module, and LCD module. The software implementation part mainly includes the design and implementation of serial communication, the design and implementation of wireless communication, the design and implementation of collection of data acquisition in each sensor module, and the design and implementation of network communication as well as the design and implementation of display unit. With the cooperation of these four parts, we can automatically sense and control the state of the environment to realize human-computer interaction.

The fast development of vehicle positioning technologies has paved the way for in-vehicle recommendation systems. Successive Point-of-Interest (POI) recommendation, one of the most common forms of in-vehicle recommendation, can help users choose places where they might visit in the future. Generally, to recommend POIs for users, raw check-in data are collected from in-vehicle devices, which raises users’ privacy concerns. To address this problem, we propose a successive POI recommendation framework with local differential privacy. The proposed framework exploits three types of influence factors for recommendation, including geographical influence, temporal influence, and transition pattern between two POIs. To protect users’ check-in data, we propose a distance-weighted location perturbation mechanism that realizes local differential privacy. Considering that different users may have different privacy requirements, we enhance this perturbation mechanism to provide personalized privacy protection. We use the EM reconstruction method to correct the error caused by perturbation on the server side. To improve the accuracy, we design an individual-weighted EM reconstruction method. Simulation results demonstrate that the proposed methods can provide strong privacy protection for users without hurting the recommendation accuracy too much.

To take advantages of location-based services (LBS) while protecting user privacy against untrusted LBS providers, privacy preserving LBS have attracted increasing attention. Considering that users in an LBS system are often equipped with resource-constrained mobile devices, most existing privacy preserving LBS methods are based on anonymization techniques. However, these existing schemes still have some privacy and efficiency limitations. In this paper, we propose a novel privacy preserving LBS scheme, which simultaneously achieves user privacy protection and high query efficiency. Specifically, we utilize the k-anonymity technique and the pseudo random function to protect the location privacy and the query message privacy of users. We design an enhanced one-round blind filter protocol (ORBFe) based on the Paillier cryptosystem to securely filter out redundant records generated by the k-anonymity technique. Compared with existing solutions, our ORBFe protocol not only ensures that users receive exactly satisfying results but also incurs a low computation and communication cost on the server side. Through the theoretical analysis and extensive experiments, we demonstrate the security and efficiency of our proposed scheme.

Multipath TCP (MPTCP) proxy is a way to provide multipath transmissions for current Internet hosts. However, previous works on MPTCP mainly focus on the scenario where one end of the connection is MPTCP-capable, which is not the case in current networks. In this article, we propose a transparent MPTCP (T-MPTCP) transmission scheme to achieve MPTCP transmission for TCP hosts at both ends. Specifically, T-MPTCP contains two components in the network: an aggregation box at the user side acting as an implicit proxy, and an aggregation server at the server side acting as an explicit proxy. The aggregation server can be deployed in different places of the network for optimized performance, and we thus propose a Proxy Location Selected (PLS) algorithm to determine the best aggregation server placement. To validate our design, T-MPTCP is deployed in a real network for experiments, whose results exhibit higher network throughput under T-MPTCP and further improvement with the PLS algorithm.

The vehicle fuel consumption gauge is a vehicle’s basic device that usually records the instantaneous as well as average fuel consumption of the vehicle, which brings a lot of convenience during the driving process. The individual real-time fuel consumption data are meaningless, but its continuous real-time fuel consumption data contains some information that may reveal the user’s privacy, which is sensitive information for users. There are hot topics on privacy protection of sensitive user information, however, most of the studies have focused on privacy protection at the time of data release and the need for a trusted third party. In this paper, we propose a negative survey-based approach that can be utilized to protect aggregated vehicle fuel consumption data against time-series-based differential attacks. Its anonymous algorithm on the user side and estimation algorithm on the server side are able to protect the user’s privacy regardless of changes in the user’s fuel consumption data. The experiment results show that our method can achieve better privacy protection with better application prospects in a simpler and more effective way.

It has been predicted that more devices will be connected to the internet network along with the development of IoT, and that will increase the complexity in the security system. The most important thing in security systems is the process of encryption and authentication. Various encryption techniques are developed to overcome security problems of the data. One of the methods used for authentication of data is Zero Knowledge Proof. This method works to identify the authenticity of someones statement to proof without showing any knowledge of the statement mentioned. This research will mainly discuss about the security of data transmission system by combining data encryption and data authentication. The proposed data encryption is using Advanced Encryption System and method for authentication of data using the Zero Knowledge Proof. This research will conduct the development methods of authentication Zero Knowledge Proof from previous research and the result was compared with the proposed method based on the simulation results transmission system client and server. Experiments will be conducted using thirty-text data, each of data will be measured on the performance of both encryption and authentication process between the previous method and proposed method. Experimental results show the performance of the proposed method has better speed to process application for security of data transmission systems, with performance to authenticate approximately 5 ms from the client side and server side.

The widespread use of Internet of Things (IoT) technology has promoted location-based service (LBS) applications. Users can enjoy various conveniences brought by LBS by providing location information to LBS. However, it also brings potential privacy threats to location information. Location data that contains private information is often transmitted among IoT networks in LBS, and such privacy information should be protected. In order to solve the problem of location privacy leakage in LBS, a location privacy protection scheme based on k -anonymity is proposed in this paper, in which the Geohash coding model and Voronoi graph are used as grid division principles. We adopt the client-server-to-user (CS2U) model to protect the user’s location data on the client side and the server side, respectively. On the client side, the Geohash algorithm is proposed, which converts the user’s location coordinates into a Geohash code of the corresponding length. On the server side, the Geohash code generated by the user is inserted into the prefix tree, the prefix tree is used to find the nearest neighbors according to the characteristics of the coded similar prefixes, and the Voronoi diagram is used to divide the area units to complete the pruning. Then, using the Geohash coding model and the Voronoi diagram grid division principle, the G-V anonymity algorithm is proposed to find k neighbors in an anonymous area so that the user’s location data meets the k -anonymity requirement in the area unit, thereby achieving anonymity protection of location privacy. Theoretical analysis and experimental results show that our method is effective in terms of privacy and data quality while reducing the time of data anonymity.

Cross-Site Scripting (XSS) is one of the most common and dangerous attacks. The user is the target of an XSS attack, but the attacker gains access to the user by exploiting an XSS vulnerability in a web application as Bridge. There are three types of XSS attacks: Reflected, Stored, and Dom-based. This paper focuses on the Stored-XSS attack, which is the most dangerous of the three. In Stored-XSS, the attacker injects a malicious script into the web application and saves it in the website repository. The proposed method in this paper has been suggested to detect and prevent the Stored-XSS. The prevent Stored-XSS Server (PSS) was proposed as a server to test and sanitize the input to web applications before saving it in the database. Any user input must be checked to see if it contains a malicious script, and if so, the input must be sanitized and saved in the database instead of the harmful input. The PSS is tested using a vulnerable open-source web application and succeeds in detection by determining the harmful script within the input and prevent the attack by sterilized the input with an average time of 0.3 seconds.

The availability of safety has always been a challenge in all fields of computing and networking, and the development of reliable security measures for low operating power systems has been a continuous task for researchers. This paper focuses on Radio Frequency Identification (RFID) systems, whose market is tremendously growing due to the increasing need for efficient supply chain and healthcare management. Two protocols are proposed that offer mutual authentication, confidentiality, forward security, anonymity, unlinkability, scalability, and resistance against the most important security attacks. They differ in storage requirements at the server side. Both proposed protocols are very efficient as the number of required elliptic curve multiplications is limited to two at the tag side. Moreover, the schemes are also more effective and secure than other related work presented in literature. In addition, we show how to extend the protocols in order to provide authentication with respect to multiple readers.

The VPN technology is used to connect client devices securely over unsecured public networks. The Connection handover and Dead Peer Detection (DPD) are the most challenging and important tasks in VPN. In this present article, a solution for connection handover is proposed which covers the pre-authentication with new credentials for a mobile device prior to connection handover. In VPN, a case where such connection handover is failed due to incomplete connection handover or several unusual events which make a VPN client to become offline or dead. To address DPD issues a solution is proposed which includes a Keep Alive Timer (KAT) server at the VPN server side. This proposed solution for DPD is far better than an already existing solution that uses a DNS server for DPD since the DNS server faces several attacks consistently. Thus, the proposed solution for DPD is more secure against such vulnerable attacks.

Face image features represent significant user privacy concerns. Face images cannot be privately transferred under existing privacy protection methods, and data across various social networks are unevenly distributed. This paper proposes a method for face image privacy protection based on federated learning and ensemble models. A federated learning model based on distributed data sets was established by means of federated learning. On the client side, a local facial recognition model was obtained by local face data training and used as the input of PcadvGAN to train PcadvGAN for several rounds. On the server side, a parameter aggregator based on a differential evolutionary algorithm was established as the discriminator of PcadvGAN server, and a client facial recognition model was ensembled simultaneously. The discriminator of the PcadvGAN server experienced mutation, crossover, and interaction with the ensemble model to reveal the optimal global weight of the PcadvGAN model. Finally, the global optimal aggregation parameter matrix of PcadvGAN was obtained by calculation. The server and the client shared the global optimal aggregation parameter matrix, enabling each client to generate private face images with high transferability and practicality. Targeted attack and non-targeted attack experiments demonstrated that the proposed method can generate high-quality, transferable, robust, private face images with only minor perturbations more effectively than other existing methods.

Many applications such as smart metering and location based services pose strong privacy requirements but achieving privacy protection at the client side is a non-trial problem as payment for the services must be computed by the server at the end of each billing period. In this paper, we propose a privacy preservation and billing scheme termed PPDIR based on delayed information release. PPDIR relies on a novel group signature mechanism and the asymmetric Rabin cryptosystem to protect the privacy of the clients and their requests, to achieve accountability and non-repudiation, and to shift the computational complexity to the server side. It adopts a secret token for anonymity and the token is updated for each client at the beginning of each billing period and securely released only to the server at the end of the billing period. Such a strategy can prevent the server from linking a client’s requests made at different billing periods. It also prevents any adversary from linking any request to any client. Note that the server is able to figure out all requests made by a client within a billing period after receiving the delayed token, which is unavoidable for billing purpose. We prove the security properties of the group signature scheme, and analyze the security strength of PPDIR. Our study indicates that PPDIR can achieve privacy-preservation, confidentiality, non-repudiation, accountability, and other security objectives. We also evaluate the performance of our scheme in terms of communication and computational overheads.

Purpose The purpose of this study is to design and implement an intelligent online proctoring system (IOPS) by using the advantage of artificial intelligence technology in order to monitor the online exam, which is urgently needed in online learning settings worldwide. As a pilot application, the authors used this system in an authentic university online exam and checked the proctoring result. Design/methodology/approach The IOPS adopts the B/S (Browser/Server) architecture. The server side is implemented with programming language C and Python and stores the identification data of all examinees and their important behavior change status, including facial expression, eye and mouth movement and speech. The browser side collects and analyzes multimodal data of the examinee writing the online test locally and transfers the examinee’s most important behavior status change data to the server. Real-time face recognition and voice detection are implemented with the support of open-source software. Findings The system was integrated into a Web-based intelligent tutoring system for school mathematics education. As a pilot application, the system was also used for online proctored exam in an undergraduate seminar in Peking University during the epidemic period in 2020. The recorded log data show that all students concentrated themselves on the exam and did not leave the camera and did not speak. Originality/value During the epidemic period of the novel coronavirus outbreak, almost all educational institutions in the world use online learning as the best way to maintain the teaching and learning schedule for all students. However, current online instruction platforms lack the function to prevent the learners from cheating in online exams and cannot guarantee the integrity and equality for all examinees as in traditional classroom exams. The literature review shows that the online proctoring system should become an important component of online exams to tackle the growing online cheating problem. Although such proctoring systems have been developed and put on the market, the practical usage of such systems in authentic exams and its effect have not been reported. Those systems are heavyweight and commercial product and cannot be freely used in education. The light-weight IOPS developed by the authors could meet the need for online exam as a stable and practical approach and could contribute to the growing online learning and distance learning.

Cyber attacks are getting progressively incessant, causing a great deal of harm. Attackers take our valuable information by compromising web application security loopholes. Dynamic content that is being incorporated into the html that has been served to the client. assume when you open a site page then you see your name so that is dynamic substance for each client who additionally at any point visits that page. We can inject input fields and they are shipped off the web worker. So ,we need to check for all information handled whose worth is reflected in some structure to get the prepared payload. Then attempt to misuse it dependent on the layouts. This paper discusses the idea of an template injection and its impact on template based web application

A subdomain is a domain that is a part of another domain. Subdomains are used to organize and navigate to various parts of your website. For example, your primary domain could be “xyz.com,” while your blog could be on a subdomain at “blog.xyz.com.” A subdomain takeover occurs when an attacker gains control over a subdomain of a target domain. Sub-domain takeover vulnerability occurs When a subdomain (subdomain.example.com) that refers to a service (eg GitHub, AWS / S3, ..) is deleted or deleted In this way, the attacker can create pages on the service in use and forward the pages to this subdomain.. If any person wants to take over, a subdomain then the person seeks to manually check one by one subdomain that takes too much time. Moreover, are there some tools available to check the subdomain takeover is possible or not? However, these tools take input as a text file, which has a particular subdomain. This means finding a subdomain with the other tools and then using one of these tools to identify subdomain takeover vulnerability. In my tools, we find the subdomain of a particular domain, then check the CNAME is available for a list of subdomains and if CNAME finds for a specific subdomain, then check the status code of the CNAME if it returns 404-status code. We might say that a particular subdomain is possible to takeover.

Streaming media server is the core system of audio and video application in the Internet; it has a wide range of applications in music recommendation. As song libraries and users of music websites and APPs continue to increase, user interaction data are generated at an increasingly fast rate, making the shortcomings of the original offline recommendation system and the advantages of the real-time streaming recommendation system more and more obvious. This paper describes in detail the working methods and contents of each stage of the real-time streaming music recommendation system, including requirement analysis, overall design, implementation of each module of the system, and system testing and analysis, from a practical scenario. Moreover, this paper analyzes the current research status and deficiencies in the field of music recommendation by analyzing the user interaction data of real music websites. From the actual requirements of the system, the functional and performance goals of the system are proposed to address these deficiencies, and then the functional structure, general architecture, and database model of the system are designed, and how to interact with the server side and the client side is investigated. For the implementation of data collection and statistics module, this paper adopts Flume and Kafka to collect user behavior data and uses Spark Streaming and Redis to count music popularity trends and support efficient query. The recommendation engine module in this paper is designed and optimized using Spark to implement incremental matrix decomposition on data streams, online collaborative topic model, and improved item-based collaborative filtering algorithm. In the system testing section, the functionality and performance of the system are tested, and the recommendation engine is tested with real datasets to show the discovered music themes and analyze the test results in detail.

In this article, a Website for medical healthcare system was designing and developing. It consists of two major sides: client- server side (front end side and back end side). The client-side is everything involved with what the user sees, it has been designed a web using HTML, CSS and JavaScript languages. The server-side is mainly how the site doing modifications and updates which indicates to the entire user can't see everything in the browser such as servers and databases. The web has been implemented and developed using ASP MVC5 and C# programming language. SQL Server languages used for the database part and it make simple ease of use for patients to their health registrations. Consequently, it has simple and straight accessibility through a group of physicians for patient records. The article interested with auspices to the patient appointments combination, billing, timetable, physical, date, and information of medication in single overall system. The results of website designed provide accessibility with easy manner of pertinent information to the management organizations for instance the Medicaid and Medicare. Furthermore, the website reduces the mistake in healthcare, and reduce the cost of delivery of healthcare. Consequently, the website prepared for utilize by nurses, physician, pharmacists and another healthcare professionals, and by patients and monitor patients using medical devices.

With the development of cloud computing, the storage and processing of massive visual media data has gradually transferred to the cloud server. For example, if the intelligent video monitoring system cannot process a large amount of data locally, the data will be uploaded to the cloud. Therefore, how to process data in the cloud without exposing the original data has become an important research topic. We propose a single-server version of somewhat homomorphic encryption cryptosystem based on confused modulo projection theorem named CMP-SWHE, which allows the server to complete blind data processing without seeing the effective information of user data. On the client side, the original data is encrypted by amplification, randomization, and setting confusing redundancy. Operating on the encrypted data on the server side is equivalent to operating on the original data. As an extension, we designed and implemented a blind computing scheme of accelerated version based on batch processing technology to improve efficiency. To make this algorithm easy to use, we also designed and implemented an efficient general blind computing library based on CMP-SWHE. We have applied this library to foreground extraction, optical flow tracking, and object detection with satisfactory results, which are helpful for building smart cities. We also discuss how to extend the algorithm to deep learning applications. Compared with other homomorphic encryption cryptosystems and libraries, the results show that our method has obvious advantages in computing efficiency. Although our algorithm has some tiny errors ( $10^{-6}$ ) when the data is too large, it is very efficient and practical, especially suitable for blind image and video processing.

Mobile advertising plays a vital role in the mobile app ecosystem. A major threat to the sustainability of this ecosystem is click fraud , i.e., ad clicks performed by malicious code or automatic bot problems. Existing click fraud detection approaches focus on analyzing the ad requests at the server side. However, such approaches may suffer from high false negatives since the detection can be easily circumvented, e.g., when the clicks are behind proxies or globally distributed. In this paper, we present AdSherlock, an efficient and deployable click fraud detection approach at the client side (inside the application) for mobile apps. AdSherlock splits the computation-intensive operations of click request identification into an offline procedure and an online procedure. In the offline procedure, AdSherlock generates both exact patterns and probabilistic patterns based on URL (Uniform Resource Locator) tokenization. These patterns are used in the online procedure for click request identification and further used for click fraud detection together with an ad request tree model. We implement a prototype of AdSherlock and evaluate its performance using real apps. The online detector is injected into the app executable archive through binary instrumentation. Results show that AdSherlock achieves higher click fraud detection accuracy compared with state of the art, with negligible runtime overhead.

In traditional biometric verification systems, personal computer stores biometric database and performs verification process. Because of limited storage, capacity, and computational power, both cloud computing and data centers provide these facilities for users and enterprises. However, shifting from user-owned and user-operated system to public and untrusted access services have raised concerns over data security, either in storage or computation phases. In this work, we propose a framework for fully privacy-preserving biometric verification with outsourcing computational tasks to the commerical public cloudsl. Firstly, privacy of the data used for biometric verification is preserved by encrypting training images. Secondly, for protecting the privacy of the biometric verification model, all correlation filter computation and verification stage are performed over encrypted biometric images in server side. Finally, privacy of the biometric verification result is preserved by sending it to the client for further investigation. Our solution provides anonymous access, unlinkability, and the confidentiality of transmitted data. I will be shown that our scheme is secure in the semi-honest server and has it reaches accuracy of 93.7% on facial dataset and 92% on fingerprint dataset.

U radu je prikazan generator koda zadužen za generisanje osnove aplikacije kako serverske, tako i klijentske strane. Serverska strana izgenerisana je u Java programskom jeziku uz oslonac na Spring radni okvir, dok je klijentska strana u AngularJS-u. Proces generisanja bazira se na definisanom UML dijagramu modela

This paper describes the problem of calculating and monitoring the balance of calories and other macronutrients. The software solution to this problem is implemented in the form of two applications. The Android application contains a whole set of functionalities, while the server side is implemented in the form of a Spring application. Calculation of calorie balance is done through recording meals and activities. The balance of calories and other macronutrients is calculated on a daily basis.

In this paper, we propose an energy-saving video compression scheme targeting to work over an IoT network where the perception layer is implemented through smartphone-based delay-tolerant network (DTN). Here, the inter-frame compression is done by a proposed progression-based frame prediction technique and a newly introduced frame-similarity measurement parameter. The inter-frame co-relation is removed by using progression method followed by identifying similarity of the frames selected by the progression method. On the other hand, the intra-frame compression is done by adopting an existing compression technique with suitable modification to make it more energy saving over its existing counterpart. Once the compressed video is transmitted over the IoT network, at the server side it is reconstructed where we use the service of a popular online face recognition API, KAIROS considering an application of disaster victim recognition. The comparative performance of the scheme is evaluated both theoretically and through ONE simulator-based simulation. Precisely, we claim that our scheme saves on average 71.46% energy over its competitors with least memory usage and acceptable reconstruction quality. Finally, the entire scheme is evaluated through testbed implementation and shown that it can recognize faces successfully from the videos transmitted over DTN-based IoT with accuracy more than 0.96 while keeping compression ratio at more than 31.

Medical record retention is a system that regulates the storage period for medical record files. Medical records officers are also involved in destroying medical records including in the retention process. The slow retention process resulted in a large number of medical record files piling up in storage racks so that the medical record space became narrow. Developing medical record retention applications using Microsoft Access. This research method is carried out by conducting a Literature Review with the Literature Study method. The data sources in this study were 3 journals by looking at the inclusion and exclusion categories. Analyze data in terms of similarities, inequalities, views, compare and summaries. The results of 3 literature study journals are medical record retention applications made with web-based methods and web-based imaging. The system design uses Flowmap, DFD (Data Flow Diagram) and ERD (Entity Relationship Diagram) and is implemented with the Microsoft Visual Basic.Net programming language and Microsoft Access as a DBMS (Database Management System). The medical record retention application is very helpful for officers in separating active and inactive medical record files, which are equipped with menus according to user needs. It is hoped that the next researchers on several related articles, further research is needed on designing applications using table data with server side processing.

The software for the e-procurement system was developed based on .NET Core RESTful API with Open API specifications. The server side uses RESTful API which ensures compatibility with the majority of clients and enables them to exchange information in JSON format. The authentication and authorization flow was implemented using OAuth open standard paired with Microsoft Identity Service. User roles and functionality were handled with a standalone service for authentication and registration that made our system efficient and scalable. Business logic was designed to be split into micro-services accessible through routing controllers. This approach allowed us to separate the responsibilities between the server and the client side. Special authorization headers passed during modification queries allowed us to control and restrict access to particular resources for unauthorized users. The distributed cache mechanism inside the data repository level was used in order to increase the responsiveness of the system. The state handling subsystem was designed utilizing Finite State Machine concepts. The developed system was verified using unit and integration tests. Prom ble s in programming 2021; 1: 03-15

Near Field Communication is a set of communication protocols for communication between two electronic devices over a distance of 4cm or less and Bluetooth is a wireless technology standard used for exchanging data between fixed and mobile devices over short distances using UHF radio waves in the industrial, scientific and medical radio bands, from 2.402 GHz to 2.480GHz, and building personal area networks(PANs). Both these protocols facilitate wireless/ internet less communication between devices that have the capabilities. This paper seeks to further expand the concept and usage of these protocols via the examination of modifications made to a Quiz Management System. The Quiz Management System, which is a solution to further advance the notion of a smart classroom setup within the context of short-range wireless technologies has already seen iterations developed on both the NFC and Bluetooth side of things.

The problem of automatic requests classification, as well as the problem of determining the routing rules for the requests on the server side, is directly connected with analysis of the user interface of dynamic web pages. This problem can be solved at the browser level, since it contains complete information about possible requests arising from interaction interaction between the user and the web application. In this paper, in order to extract the classification features, using data from the request execution context in the web client is suggested. A request context or a request trace is a collection of additional identification data that can be obtained by observing the web page JavaScript code execution or the user interface elements changes as a result of the interface elements activation. Such data, for example, include the position and the style of the element that caused the client request, the JavaScript function call stack, and the changes in the page's DOM tree after the request was initialized. In this study the implementation of the Chrome Developer Tools Protocol is used to solve the problem at the browser level and to automate the request trace selection.

Cookies were originally introduced as a way to provide state awareness to websites, and they are now one of the backbones of the current web. However, their use is not limited to store the login information or to save the current state of user browsing. In several cases, third-party cookies are deliberately used for web tracking, user analytics, and for online advertisement, with the subsequent privacy loss for the end-users. However, cookies are not the only technique capable of retrieving the users’ browsing history. In fact, history sniffing techniques are capable of tracking the users’ browsing history without relying on any specific code in a third-party website, but only on code executed within the visited site. Many sniffing techniques have been proposed to date, but they usually have several limitations, and they are not able to differentiate between multiple possible states within the target application. We propose B aking T imer , a new history-sniffing technique based on timing the execution of server-side request processing code. This method is capable of retrieving partial or complete user browsing history, it does not require any permission, and it can be performed through both first- and third-party scripts. We studied the impact of our timing side-channel attack to detect prior visits to websites and discovered that it was capable of detecting the users’ state in more than half of the 10K websites analyzed, which is the largest test performed to date to test this type of technique. We additionally performed a manual analysis to check the capabilities of the attack to differentiate between three states: never accessed, accessed, and logged in. Moreover, we performed a set of stability tests to verify that our time measurements are robust with respect to changes both in the network RTT and in the servers workload. This extended version additionally includes a comprehensive analysis of existing countermeasures, starting from its evolution/adoption, and finishing with a large-scale experiment to asset the repercussions on the presented technique.

En la actualidad, la seguridad representa un factor clave en el desarrollo de aplicaciones web de tipo empresarial, debido al hecho de administrar de manera eficiente un número alto de solicitudes efectuadas por varios usuarios de manera simultánea, en ese sentido, la autentificación y autorización juegan un rol importante debiendo ser consideradas en el diseño de la funcionalidad durante el desarrollo de la aplicación. En este artículo, proponemos la implementación de una estrategia de seguridad basada en el framework Spring Security, abordando los temas de autentificación de usuarios, administración de roles y permisos de acceso a componentes, y manejo de sesiones; todo ello, bajo un esquema de arquitectura multicapa aplicando los conceptos del patrón MVC (Modelo-Vista-Controlador) y el paradigma de la programación orientada a objetos. La fácil integración y configuración de Spring Security con otros componentes de software, también se discute en este artículo; permitiendo la construcción de soluciones confiables, robustas y seguras de lado del servidor. Asimismo, la estrategia de seguridad que se describe en este artículo, puede ser considerada para la construcción de API REST, Microservicios o sistemas bidireccionales basados en eventos.

Various big data sets are recorded on the server side of computer system. The big data are well defined as a volume, variety, and velocity (3V) model. The 3V model has been proposed by Gartner, Inc. as a first press release. 3V model means the volume, variety, and velocity in terms of data. The big data have 3V in well balance. Then, there are various categories in terms of the big data, e.g., sensor data, log data, customer data, financial data, weather data, picture data, movie data, and so on. In particular, the fault big data are well-known as the characteristic log data in software engineering. In this paper, we analyze the fault big data considering the unique features that arise from big data under the operation of open source software. In addition, we analyze actual data to show numerical examples of reliability assessment based on the results of multiple regression analysis well-known as the quantification method of the first type.

Federated learning has emerged as an advanced privacy-preserving learning technique for mobile edge computing, where the model is trained in a decentralized manner by the clients, preventing the server from directly accessing those private data from the clients. This learning mechanism significantly challenges the attack from the server side. Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i.e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client. To analyze the privacy leakage of federated learning, this paper gives the first attempt to explore user-level privacy leakage by the attack from a malicious server. We propose a framework incorporating GAN with a multi-task discriminator, called multi-task GAN - Auxiliary Identification (mGAN-AI), which simultaneously discriminates category, reality, and client identity of input samples. The novel discrimination on client identity enables the generator to recover user specified private data. Unlike existing works interfering the federated learning process, the proposed method works “invisibly” on the server side. Furthermore, considering the anonymization strategy for mitigating mGAN-AI, we propose a beforehand linkability attack which re-identifies the anonymized updates by associating the client representatives. A novel siamese network fusing the identification and verification models is developed for measuring the similarity of representatives. The experimental results demonstrate the effectiveness of the proposed approaches and the superior to the state-of-the-art.

Thanks to the capabilities of the built-in sensors of smart devices, mobile crowd-sensing (MCS) has become a promising technique for massive data collection. In this paradigm, the service provider recruits workers (i.e., common people with smart devices) to perform sensing tasks requested by the consumers. To efficiently handle workers’ recruitment and task allocation, several factors have to be considered such as the quality of the sensed data that the workers can deliver and the different tasks locations. This allocation becomes even more challenging when the MCS tries to efficiently allocate multiple tasks under limited budget, time constraints, and the uncertainty that selected workers will not be able to perform the tasks. In this paper, we propose a service computing framework for time constrained-task allocation in location based crowd-sensing systems. This framework relies on (1) a recruitment algorithm that implements a multi-objective task allocation algorithm based on Particle Swarm Optimization, (2) queuing schemes to handle efficiently the incoming sensing tasks in the server side and at the end-user side, (3) a task delegation mechanism to avoid delaying or declining the sensing requests due to unforeseen user context, and (4) a reputation management component to manage the reputation of users based on their sensing activities and task delegation. The platform goal is to efficiently determine the most appropriate set of workers to assign to each incoming task so that high quality results are returned within the requested response time. Simulations are conducted using real datasets from Foursquare1 and Enron email social network.2 Simulation results show that the proposed framework maximizes the aggregated quality of information, reduces the budget and response time to perform a task and increases the average recommenders’ reputation and their payment.