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Due to the appeal of Generative Adversarial Networks (GANs), synthetic face images generated with GAN models are difficult to differentiate from genuine human faces and may be used to create counterfeit identities. However, these face images contain artifacts presented in irises owing to the irregularity of highlights between the left and right irises. Adversaries can utilize PS documents of these images trying to conceal the artifacts of images, which makes it more challenging to distinguish between pristine and fake images. In order to tackle this, our research introduces a complete dataset and analytical tools that make a substantial contribution to multimedia forensics. This allows for the authentication of documents despite common alterations in the PS documents. Owing to the lack of large-scale reference IRIS datasets in the PS scenario, this study provided a pioneering dataset aiming to set a standard for multimedia forensic investigations. Given face images, we extracted iris images using the Dlib (King, J Mach Learn Res 10(60):1755–1758, 2020) and EyeCool (Wang et al. 2021) models, as described in Guo et al. (2022). However, in some cases, the potential eyelid occlusion phenomenon resulted in incomplete iris images. We utilized a hypergraph convolution-based image inpainting technique to complete the missing pixels in the extracted iris images, thus uncovering the intricate relationships within the iris data. To evaluate the IRIS image dataset and highlight associated issues, we conducted a series of analyses using Siamese Neural Networks, including ResNet50, Xception, VGG16, and MobileNet-v2, to measure the similarities between authentic and synthetic human iris images. Our SNN model, with four different backbones, effectively differentiated between genuine and synthetic iris images. For instance, using the Xception network, we achieved 56.76% similarity in IRISes for synthetic images and 92.77% similarity in IRISes for real images. The effectiveness of our approach was demonstrated by the similarity scores obtained from all SNN architectures, which showed a significant difference between the GAN-generated images from ProGAN or StyleGAN and the original PS iris photos. The similarity scores resulting from StyleGAN are higher than those of the ProGAN architecture, but at its highest, it is 76%, while for the pristine images, it ranges from 85% to 95%. This discrepancy can be utilized in order to distinguish between pristine and GAN-generated images. [ABSTRACT FROM AUTHOR]

Wireless Multimedia Sensor Network (WMSN) is a network of interconnected sensor nodes equipped with multimedia devices capable to retrieve multimedia as well as scalar sensor data in smart city scenarios. With the progress of smart city development, WMSNs are becoming points of interest. The existing research works on the security of WMSNs show some significant centralized solutions; they are prone to denial of service attacks, central failure, and privacy breaches. Also, these solution are having limitations because of their centralized nature.Therefore, the the proposed scheme adopted the concept of decentralization to achieve more effective and ubiquitous results. As per our best knowledge, we introduce the first decentralized privacy-preserving authentication framework for WMSNs to address the energy and storage challenges. We call our solution PRIvacy-preserving Blockchain-based Authentication for multimeDIa networks (PRIBADI). PRIBADI uses a blockchain framework for authenticated communication between sensor nodes and base stations (as a trusted authority) by using certification and access authentication schemes. We elect trustworthy cluster heads based on weight metrics and these cluster heads forward the information to the Trusted Authority (TA). Subsequently, TA forward large data to the cloud for storage, and the information relevant to certificates and hashes are recorded on the distributed blockchain. Moreover, TA revokes the certificates of malicious sensor nodes and eliminates them from the blockchain. We run a set of experiments on PRIBADI and compare the results with the state-of-the-art models. We use the evaluation criteria of detection accuracy, operation, and energy consumption. We find that PRIBADI is 31.7% better than the existing models. [ABSTRACT FROM AUTHOR]

Industrial applications and automation controls are closely connected with heterogeneous Wireless Sensor Network (WSN); thus, Industrial Internet-of Thing (IIoT) enhances the productivity, scalability, and flexibility of the operations. However, in many cases such as tracking a device or service localization becomes very crucial to state back the operations at times. Existing literature show a various localization solutions, but they face the drawbacks of high energy consumption, and non-coherence in heterogeneity. Besides, in a WSN-based IIoT, non-cooperativeness exists among nodes due to various system and environmental parameters, which make the system resource-exhaustive. In this paper, we introduce a novel localization algorithm that addresses the above-mentioned problems. We call our proposed algorithm RAnge-free Power efficient COoperative Localization (RAPCOL). Specifically, the use of co-operative beacon nodes to broadcast the information to Base Station and low energy consumption highlights the novelty of the work. RAPCOL uses weight metrics for selecting optimal cooperative beacon nodes to prolong the network lifetime. We also introduce an Improvised Particle Swarm Optimization (IPSO) that credits in the contribution in RAPCOL. We run a overall nine sets of experiments to analyze the localization accuracy, effect of beacon nodes, sensor nodes, network connectivity, and sensing field, error frequency, residual energy, time, and network lifetime. A comparative study with the existing localization models shows that RAPCOL is 30 % better than the existing models in terms of accuracy and resource consumption. We observe stable performance of RAPCOL with a differentiated effect of beacon nodes and sensor nodes. We also observe that our proposed IPSO 20 % better in fast convergence to the optimal solution. Though RAPCOL localization time is 12 % higher than other existing protocols, RAPCOL's accuracy and energy saving mechanism make it efficient for IIoTs. [ABSTRACT FROM AUTHOR]

The Vehicular Cloud Computing (VCC) provides significant benefits (e.g., higher resource availability, and low communication latency) to vehicular applications. However, still there exists latency related issues with applications that require real-time response, and it is difficult to achieve when using VCC. Therefore, the use of fog computing that provides the next level service of cloud computing from the centre to edge networks is envisioned. Apart from low latency, fog computing also supports mobility, location-awareness, and real-time data handling, which enables ubiquitous connectivity between fog nodes and smart vehicles. However, there remain several security and privacy challenges while using fog computing in VANET applications. To address these challenges, we propose the design of an authenticated message-exchange scheme for fog-assisted VCC (called AME-VCC). The proposed scheme ensures mutual authentication along with anonymity, which enables accountable privacy. The correctness of the protocol and its security is proved in the random oracle model, which indicates that it is provably secure under any probabilistic polynomial time adversary. Moreover, the comparative study of computation cost is done to prove the claim of efficiency. The analysis of security and performance indicates that AME-VCC is computationally efficient protocol, and it ensures authenticated secure message exchange with anonymity and unlinkability. [ABSTRACT FROM AUTHOR]

Nowadays, the number of Distributed Denial of Service (DDoS) attacks is growing rapidly. The aim of these type of attacks is to make the prominent and critical services unavailable for legitimate users. HTTP flooding is one of the most common DDoS attacks and because of its implementation in application layer, it is difficult to detect and prevent by the current defense mechanisms. This attack not only makes the web servers unavailable, but consumes the computational resources of the network equipment and congests communication links. Recently, the advent of Software Defined Networking (SDN) paradigm has enabled the network providers to detect and mitigate application layer DDoS attacks such as HTTP flooding. In this paper, we propose a defense mechanism named HTTPScout which leverages the benefits of SDN together with Machine Learning (ML) techniques to detect and mitigate HTTP flooding attack. HTTPScout is implemented as a security module in RYU controller and monitors the behavior of HTTP traffic flows. Upon detecting a malicious flow, it blocks the source of the attack at the edge switch and preserves the network resources from the adversarial effects of the attack. Simulation results confirm that HTTPScout brings a significant improvement of 64% in bandwidth consumption and 80% in the number of forwarding rules compared to normal SDN. [ABSTRACT FROM AUTHOR]

The Android application framework has a pervasive presence. In early 2021, Android has over 70 % share of the operating system mobile market (according to GlobalStats). Components are the main building blocks of Android Applications. These blocks communicate via a rich Inter-Component Communication (ICC) model rather than the traditional inter-process communication model. Intents, Intent-filters, and their Intents resolution (matching) algorithm are main elements of the ICC. However, the Intent resolution algorithm is not robust enough and has flaws that can lead to security breaches. In this paper, we present DLAIR, as an enrichment of the Intent resolution algorithm to overcome its security issues. To this end, we start by presenting a formal model to express and validate the ICC semantics. This includes defining key properties guaranteeing consistent and realistic semantic states. We then demonstrate how the semantics can be used to formally validate ICC aspects and to express and check ICC system updates. We verified our proposed model and all its lemmas and theorems in the Coq Proof Assistant, a machine-assisted verification tool. We extend our semantics to develop DLAIR which is assisted by a heuristic, and lightweight tool, LekInt. This tool identifies suspicious execution paths responsible for intent based sensitive user-information leakage. On a dataset of 2000 real-world apps, we evaluated LekInt against Flowdroid, a state-of-the-art information leakage analysis tool. Experiments show that LekInt is more effective and efficient than Flowdroid which has a higher false-negative rate and lower false-positive rate than LekInt. Considering the dynamic context in which LekInt is designed to work, the advantage of efficiency overcomes the disadvantage of higher false-negative. [ABSTRACT FROM AUTHOR]

In recent years, the Underwater Internet of Things (IoUT) has become a popular technology for exploring the underwater environment. IoUT enables administrators to explore and monitor underwater environmental phenomena from anywhere in the world where there is Internet access. Due to the harsh underwater environment, the reliability of communication between sensor nodes deteriorates, causing certain performance issues such as higher packet loss rate and long end-to-end delay. Therefore, it is essential to manage the communications between the sensors to address these problems in order to improve the QoS. Software-defined networking (SDN) is one of the most promising architectures for providing efficient network management by decoupling the data plane from the control plane of the network. This paper proposes a new QoS routing technique for SDN-based IoUT aiming at improving QoS by establishing reliable paths between sensor nodes. To do this, the controller gathers the 3D coordinates of each underwater sensor in order to compute the distance between the nodes. Then, it estimates the reliability of each link by using underwater acoustic equations. Finally, it calculates the most reliable path with minimum delay and installs the path on the nodes located along it. The experimental results show that our mechanism significantly outperforms other non-SDN approaches in terms of several performance measures ranging from packet loss ratio and end-to-end delay to energy consumption. [ABSTRACT FROM AUTHOR]

Several ongoing research efforts aim to design potential Future Internet Architectures, among which Named-Data Networking (NDN) introduces a shift from the existing host-centric Internet Protocol-based Internet infrastructure towards a content-oriented one. However, researchers have identified some design limitations in NDN, among which some enable to build up a new type of Distributed Denial of Service attack, better known as Interest Flooding Attack (IFA). In IFA, an adversary issues not satisfiable requests in the network to saturate the Pending Interest Table (PIT) of NDN routers and prevent them from properly handling the legitimate traffic. Researchers have been trying to mitigate this problem by proposing several detection and reaction mechanisms, but all the mechanisms proposed so far are not highly effective and, on the contrary, heavily damage the legitimate traffic. In this paper, we propose a novel mechanism for IFA detection and mitigation, aimed at decreasing the memory consumption of the PIT by effectively reducing the malicious traffic that passes through each NDN router. In particular, our protocol exploits an effective management strategy on the PIT, through which the Malicious Interest (MIs) already stored in the PIT are removed and the new incoming MIs are dropped. In addition, the proposed countermeasure provides an additional security wall on the edges of the network to detect and mitigate the attack as early as possible and improve the network health, i.e., routers PIT occupancy during IFA. To evaluate the effectiveness of our work, we implemented the proposed countermeasure on the open-source ndnSIM simulator and compared its effectiveness with the state of the art. The results show that our proposed countermeasure effectively reduces the IFA damages both in terms of preserved legitimate traffic and availability of routers PIT. Considering the legitimate traffic, the amount of Benign Interests preserved by our approach increases from 5% to 40% with respect to the preservation guaranteed by the state-of-the-art solutions. Concerning the routers PIT availability, our approach guarantees that the 97% of the PIT size is left free for handling the legitimate traffic. [ABSTRACT FROM AUTHOR]

Android is currently the most widespread operating system (OS) worldwide, but also the most prone to attacks. Despite the challenges faced by Industry and Academia to improve the Android OS security, it still has several vulnerabilities. Among those, the severity of the Next-Intent Vulnerability (NIV) can be immediately grasped. Android apps are made of components, which by default are private and cannot be targeted by other apps on the same phone. However, NIV allows any app to access the private components of a different app, eventually generating a crash or stealing sensitive data. NIV occurs when there is a chain of calls among different components based on the Intent messaging model and there is no control over the reliability of the first component triggering the call. NIV was first detected in 2013, but it is still an open issue. In this paper, we present Next-Intent Vulnerability Detector ( N I V D ), a novel approach to detect NIV in Android apps by relying on type systems. N I V D applies the inference rules of its type system to the app execution paths containing a sequence of calls to three NIV-related Android APIs. Compared to the state-of-the-art, N I V D is faster and more efficient, without losing precision in detecting NIV. Finally, through N I V D Google Photos was found to be vulnerable, and we disclosed the finding on the Google official bug report website (issue number 124342801). [ABSTRACT FROM AUTHOR]

The widespread adoption of smartphones dramatically increases the risk of attacks and the spread of mobile malware, especially on the Android platform. Machine learning-based solutions have been already used as a tool to supersede signature-based anti-malware systems. However, malware authors leverage features from malicious and legitimate samples to estimate statistical difference in-order to create adversarial examples. Hence, to evaluate the vulnerability of machine learning algorithms in malware detection, we propose five different attack scenarios to perturb malicious applications (apps). By doing this, the classification algorithm inappropriately fits the discriminant function on the set of data points, eventually yielding a higher misclassification rate. Further, to distinguish the adversarial examples from benign samples, we propose two defense mechanisms to counter attacks. To validate our attacks and solutions, we test our model on three different benchmark datasets. We also test our methods using various classifier algorithms and compare them with the state-of-the-art data poisoning method using the Jacobian matrix. Promising results show that generated adversarial samples can evade detection with a very high probability. Additionally, evasive variants generated by our attack models when used to harden the developed anti-malware system improves the detection rate up to 50% when using the generative adversarial network (GAN) method. [ABSTRACT FROM AUTHOR]

Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the K-nearest neighboring algorithm to defend against such attacks. However, such an approach can suffer from low to miss-classification rate accuracy. In this paper, we design an architecture to tackle the Android malware detection problem in IoT systems. We develop an attack mechanism based on silhouette clustering method, modified for mobile Android platforms. We proposed two convolutional neural network-type deep learning algorithms against this Silhouette Clustering-based Label Flipping Attack. We show the effectiveness of these two defense algorithms—label-based semi-supervised defense and clustering-based semi-supervised defense—in correcting labels being attacked. We evaluate the performance of the proposed algorithms by varying the various machine learning parameters on three Android datasets: Drebin, Contagio, and Genome and three types of features: API, intent, and permission. Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19% accuracy when compared with the state-of-the-art method in the literature. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) employs a large number of sensors and actuators to collect and act upon data for its smart functionalities. These devices are considered as a part of the Low-power and Lossy Networks due to their use of low power embedded hardware and computationally constrained nature. For synchronization and utility, these devices are often clubbed together logically to form groups. To maintain data confidentiality within a group, a shared symmetric key called the Group Key (GK) is used by all the group members. The GK must be redistributed upon joining and leaving of a group member to maintain forward and backward secrecy. However, the key management (i.e., generation and distribution) process causes overhead which consumes the scarce network resources. In this paper, we propose TARE, a novel Topology Adaptive Re-kEying (TARE) scheme for lightweight and secure group communication. TARE integrates the principles of routing tree mapped logical key tree and local derivation of the key over the an IPv6 Routing Protocol for low-power and Lossy networks in an original way. TARE takes into consideration the current routing topology and makes maximum energy reduction as the premise for its choice of key derivation and distribution methods, thus, it reduces the network energy consumption while maintaining key secrecy and data confidentiality. In particular, TARE provides the following advantages: (1) lower network overhead and bandwidth utilization in key management and re-distribution operations, (2) effective against the network mobility in scalable IoT networks, (3) secure group communications in network against attacks such as man-in-the-middle and eavesdropping, and (4) data confidentiality by ensuring backward and forward secrecy in key distribution method. We evaluate the performance of TARE and compare it with existing schemes. Our results show the effectiveness of TARE regarding energy consumption, bandwidth utilization, and the number of encrypted message transmissions during the re-keying operations. [ABSTRACT FROM AUTHOR]

The idea of Internet of Things (IoT) is that many of the live objects (e.g., appliances) in the network are accessible, sensed, and interconnected. However, energy-constrained IoT nodes limit the performance of the IoT network. Hence, preserving energy in IoT network requires utmost attention. Unequal clustering is commonly considered as one of the efficient energy saving technique. Here, the traffic load is evenly distributed among the nodes using variable size clusters across the network. However, none of the existing solutions considered (1) realistic factors like fading model, routing protocol etc., or (2) optimization of cluster radius while devising clustering structure. The contribution of this paper is two-fold. First, we analyze the maximization of network lifetime by balancing the energy consumption among Cluster Heads (CHs). We found that cluster radius of each level has significant role in maximization of network lifetime. Second, to meet the requirement of maximization of network lifetime, this paper proposes a novel Lifetime Maximizing optimal Clustering Algorithm (LiMCA) for battery-powered IoT devices. Particularly, LiMCA includes a novel stochastic deployment scheme for Member Nodes (MNs) and CHs and a training protocol to train CHs and MNs about their coarse-grain location. Extensive simulation study shows that our algorithm improves the network lifetime by more than 30%, compared to other existing approaches. [ABSTRACT FROM AUTHOR]

In the current decade, doing the search on massive data to find "hidden" and valuable information within it is growing. This search can result in heavy processing on considerable data, leading to the development of solutions to process such huge information based on distributed and parallel processing. Among all the parallel programming models, one that gains a lot of popularity is MapReduce. The goal of this paper is to survey researches conducted on the MapReduce framework in the context of its open-source implementation, Hadoop, in order to summarize and report the wide topic area at the infrastructure level. We managed to do a systematic review based on the prevalent topics dealing with MapReduce in seven areas: (1) performance; (2) job/task scheduling; (3) load balancing; (4) resource provisioning; (5) fault tolerance in terms of availability and reliability; (6) security; and (7) energy efficiency. We run our study by doing a quantitative and qualitative evaluation of the research publications' trend which is published between January 1, 2014, and November 1, 2017. Since the MapReduce is a challenge-prone area for researchers who fall off to work and extend with, this work is a useful guideline for getting feedback and starting research. [ABSTRACT FROM AUTHOR]

Click Hijacking (clickjacking) is emerging as a web-based threat on the Internet. The prime objective of clickjacking is stealing user clicks. An attacker can carry out a clickjacking attack by tricking the victim into clicking an element that is barely visible or completely hidden. By stealing the victim's clicks, an attacker could entice the victim to perform an unintended action from which the attacker can benefit. These actions include online money transactions, sharing malicious website links, initiate social networking links, etc. This paper presents an anatomy of advanced clickjacking attacks not yet reported in the literature. In particular, we propose new class of clickjacking attacks that employ SVG filters and create various effects with SVG filters. We demonstrate that current defense techniques are ineffective to deal with these sophisticated clickjacking attacks. Furthermore, we develop a novel detection method for such attacks based on the behavior (response) of a website active content against the user clicks (request). In our experiments, we found that our method can detect advanced Scalable Vector Graphics (SVG)-based attacks where most of the contemporary tools fail. We explore and utilize various common and distinguishing characteristics of malicious and legitimate web pages to build a behavioral model based on Finite State Automaton. We evaluate our proposal with a sample set of 78,000 web pages from various sources, and 1000 web pages known to involve clickjacking. Our results demonstrate that the proposed solution enjoys good accuracy and a negligible percentage of false positives (i.e., 0.28%), and zero false negatives in distinguishing clickjacking and legitimate websites. [ABSTRACT FROM AUTHOR]

A distributed denial of service (DDoS) attack on any of the major components (e.g., controller, switches, and southbound channel) of software defined networking (SDN) architecture is a critical security threat. For example, the breakdown of controller could disrupt the data communication in the whole SDN network. A possible way to perform DoS is to generate a large number of new, but short length traffic flows. These flows will trigger malicious flooding requests to overload the controller and causes overflow in flow tables at SDN switches. In this paper, we propose two lightweight and practically feasible countermeasures against two different types of DDoS attacks called Route Spoofing and Resource Exhaustion in SDN networks. For Route Spoofing attack, we introduce a technique called "selective blocking", which stops an adversary node from maliciously using other users active communication routes. To countermeasure Resource Exhaustion attack, we propose a solution called "periodic monitoring", which detects adversary nodes based on the traffic analysis statistics that are gathered within a time window. We implement and perform result analysis of the attacks and their proposed countermeasures. When using our proposed countermeasures in the target SDN scenarios, the simulation results indicate an adequate reduction in bandwidth consumption and processing delay of new request, and it also depicts substantial gain in packet delivery rate. Additionally, we present the receiver operating characteristic curve, which shows the sensitivity and specificity of our countermeasures along with their detection accuracy. [ABSTRACT FROM AUTHOR]

An Internet of Things (IoT) architecture generally consists of a wide range of Internet-connected devices or things such as Android devices, and devices that have more computational capabilities (e.g., storage capacities) are likely to be targeted by ransomware authors. In this paper, we present a machine learning based approach to detect ransomware attacks by monitoring power consumption of Android devices. Specifically, our proposed method monitors the energy consumption patterns of different processes to classify ransomware from non-malicious applications. We then demonstrate that our proposed approach outperforms K-Nearest Neighbors, Neural Networks, Support Vector Machine and Random Forest, in terms of accuracy rate, recall rate, precision rate and F-measure. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Multi-Constraint Quality-of-Experience (QoE) centric Routing (MCQR) technique for efficient real-time video streaming over Mobile Ad-hoc Networks (MANETs). The dynamic estimation of link quality metric during MCQR route discovery process is the key of enhancing QoE at end user. MCQR limits end-to-end delay and jitter of received data packets by selecting long lifetime routes with maximum permissible delay. This minimizes the distortion caused fluctuations in perceived video. To prove the effectiveness of MCQR for real-time video streaming, we have designed a hybrid MANET consisting of simulated nodes and physical machines. In our hybrid network, the source and destination nodes are video traffic generators and receivers running on physical devices. Emulation results are obtained using real-time video streaming over high fidelity virtual wireless network, and conforming that the MCQR protocol provides satisfactory QoE to end users as compared to existing similar techniques. The packet delivery ratio of MCQR is higher than 90 % and its mean opinion score is higher than three (i.e., satisfactory) even when mobility and shadowing effects are kept high in target network scenarios. These improvements are achieved along with still maintaining similar or better routing overhead and processing delays. [ABSTRACT FROM AUTHOR]

Current trends in distributed denial of service (DDoS) attacks show variations in terms of attack motivation, planning, infrastructure, and scale. 'DDoS-for-Hire' and 'DDoS mitigation as a Service' are the two services, which are available to attackers and victims, respectively. In this work, we provide a fundamental difference between a 'regular' DDoS attack and an 'extreme' DDoS attack. We conduct DDoS attacks on cloud services, where having the same attack features, two different services show completely different consequences, due to the difference in the resource utilization per request. We study various aspects of these attacks and find out that the DDoS mitigation service's performance is dependent on two factors. One factor is related to the severity of the 'resource-race' with the victim web-service. Second factor is 'attack cooling down period' which is the time taken to bring the service availability post detection of the attack. Utilizing these two important factors, we propose a supporting framework for the DDoS mitigation services, by assisting in reducing the attack mitigation time and the overall downtime. This novel framework comprises of an affinity-based victim-service resizing algorithm to provide performance isolation, and a TCP tuning technique to quickly free the attack connections, hence minimizing the attack cooling down period. We evaluate the proposed novel techniques with real attack instances and compare various attack metrics. Results show a significant improvement to the performance of DDoS mitigation service, providing quick attack mitigation. The presence of proposed DDoS mitigation support framework demonstrated a major reduction of more than 50% in the service downtime. [ABSTRACT FROM AUTHOR]

In this paper, we propose two efficient and privacy-preserving data aggregation protocols for WSNs: PASKOS (Privacy preserving based on Anonymously Shared Keys and Omniscient Sink) and PASKIS (Privacy preserving based on Anonymously Shared Keys and Ignorant Sink)-requiring low overhead. Both protocols guarantee privacy preservation and a high data-loss resilience. In particular, PASKOS effectively protects the privacy of any node against other nodes, by requiring O(log N) communication cost in the worst case and O(1) on average, and O(1) as for memory and computation. PASKIS can even protect a node's privacy against a compromised sink, requiring only O(1) overhead as for computation, communication, and memory; however, these gains in efficiency are traded-off with a (slightly) decrease in the assured level of privacy. A thorough analysis and extensive simulations demonstrate the superior performance of our protocols against existing solutions in terms of privacy-preserving effectiveness, efficiency, and accuracy of computed aggregation. [ABSTRACT FROM AUTHOR]