Showing papers on "Data aggregator published in 2019"
TL;DR: A practical privacy-preserving data aggregation scheme is proposed without TTP, in which the users with some extent trust construct a virtual aggregation area to mask the single user's data, and meanwhile, the aggregation result almost has no effect for the data utility in large scale applications.
Abstract: The real-time electricity consumption data can be used in value-added service such as big data analysis, meanwhile the single user's privacy needs to be protected. How to balance the data utility and the privacy preservation is a vital issue, where the privacy-preserving data aggregation could be a feasible solution. Most of the existing data aggregation schemes rely on a trusted third party (TTP). However, this assumption will have negative impact on reliability, because the system can be easily knocked down by the denial of service attack. In this paper, a practical privacy-preserving data aggregation scheme is proposed without TTP, in which the users with some extent trust construct a virtual aggregation area to mask the single user's data, and meanwhile, the aggregation result almost has no effect for the data utility in large scale applications. The computation cost and communication overhead are reduced in order to promote the practicability. Moreover, the security analysis and the performance evaluation show that the proposed scheme is robust and efficient.
196 citations
TL;DR: This work proposes APPA: a device-oriented Anonymous Privacy-Preserving scheme with Authentication for data aggregation applications in fog-enhanced IoT systems, which also supports multi-authority to manage smart devices and fog nodes locally.
170 citations
TL;DR: EFFECT, an efficient flexible privacy-preserving aggregation scheme with authentication in smart grid, achieves both data source authentication and data aggregation in high efficiency and can satisfy the desired security requirements of smart grid.
Abstract: Smart grid is considered as a promising approach to solve the problems of carbon emission and energy crisis. In smart grid, the power consumption data are collected to optimize the energy utilization. However, security issues in communications still present practical concerns. To cope with these challenges, we propose EFFECT, an efficient flexible privacy-preserving aggregation scheme with authentication in smart grid. Specifically, in the proposed scheme, we achieve both data source authentication and data aggregation in high efficiency. Besides, in order to adapt to the dynamic smart grid system, the threshold for aggregation is adjusted according to the energy consumption information of each particular residential area and the time period, which can support fault-tolerance while ensuring individual data privacy during aggregation. Detailed security analysis shows that our scheme can satisfy the desired security requirements of smart grid. In addition, we compare our scheme with existing schemes to demonstrate the effectiveness of our proposed scheme in terms of low computational complexity and communication overhead.
114 citations
TL;DR: A privacy-preserving heath data aggregation scheme that securely collects health data from multiple sources and guarantee fair incentives for contributing patients is proposed and combines Boneh–Goh–Nissim cryptosystem and Shamir’s secret sharing to keep data obliviousness security and fault tolerance.
Abstract: With rapid development of e-healthcare systems, patients that are equipped with resource-limited e-healthcare devices (Internet of Things) generate huge amount of health data for health management. These health data possess significant medical value when aggregated from these distributed devices. However, efficient health data aggregation poses several security and privacy issues such as confidentiality disclosure and differential attacks, as well as patients may be reluctant to contribute their health data for aggregation. In this paper, we propose a privacy-preserving heath data aggregation scheme that securely collects health data from multiple sources and guarantee fair incentives for contributing patients. Specifically, we employ signature techniques to keep fair incentives for patients. Meanwhile, we add noises into the health data for differential privacy. Furthermore, we combine Boneh–Goh–Nissim cryptosystem and Shamir’s secret sharing to keep data obliviousness security and fault tolerance. Security and privacy discussions show that our scheme can resist differential attacks, tolerate healthcare centers failures, and keep fair incentives for patients. Performance evaluations demonstrate cost-efficient computation, communication and storage overhead.
85 citations
TL;DR: The security, privacy, and efficiency challenges in data processing for mobile edge computing are studied, and the opportunities to enhance data security and improve computational efficiency with the assistance of edge Computing are discussed.
Abstract: As we are moving toward the Internet of Things (IoT) era, the number of connected physical devices is increasing at a rapid pace. Mobile edge computing is emerging to handle the sheer volume of produced data and reach the latency demand of computation-intensive IoT applications. Although the advance of mobile edge computing on service latency has been well studied, security and efficiency on data usage in mobile edge computing have not been clearly identified. In this article, we examine the architecture of mobile edge computing and explore the potential of utilizing mobile edge computing to enhance data analysis for IoT applications while achieving data security and computational efficiency. Specifically, we first introduce the overall architecture and several promising edge-assisted IoT applications. We then study the security, privacy, and efficiency challenges in data processing for mobile edge computing, and discuss the opportunities to enhance data security and improve computational efficiency with the assistance of edge computing, including secure data aggregation, secure data deduplication, and secure computational offloading. Finally, several interesting directions on edge-empowered data analysis are presented for future research.
77 citations
TL;DR: In this paper, a distributed collision-avoidance scheduling (DCAS) algorithm is proposed to address the MLCAMDAS-MC problem in distributed WSNs, where the sensors are considered to be assigned the channels and the data are compressed with a flexible aggregation ratio.
Abstract: In wireless sensor networks (WSNs), the sensed data by sensors need to be gathered, so that one very important application is periodical data collection. There is much effort which aimed at the data collection scheduling algorithm development to minimize the latency. Most of previous works investigating the minimum latency of data collection issue have an ideal assumption that the network is a centralized system , in which the entire network is completely synchronized with full knowledge of components. In addition, most of existing works often assume that any (or no) data in the network are allowed to be aggregated into one packet and the network models are often treated as tree structures. However, in practical, WSNs are more likely to be distributed systems , since each sensor’s knowledge is disjointed to each other, and a fixed number of data are allowed to be aggregated into one packet. This is a formidable motivation for us to investigate the problem of minimum latency for the data aggregation without data collision in the distributed WSNs when the sensors are considered to be assigned the channels and the data are compressed with a flexible aggregation ratio, termed the minimum-latency collision-avoidance multiple-data-aggregation scheduling with multi-channel (MLCAMDAS-MC) problem. A new distributed algorithm, termed the distributed collision-avoidance scheduling (DCAS) algorithm, is proposed to address the MLCAMDAS-MC. Finally, we provide the theoretical analyses of DCAS and conduct extensive simulations to demonstrate the performance of DCAS.
73 citations
TL;DR: A redundancy removal strategy is proposed, which performs mining on collected data to select the appropriate information before forwarding to a base station or a cluster head in the WSN.
Abstract: In order to give a complete description of an environment or to make a robust decision, a number of observations must be collected and combined from multiple sensor nodes. In these large collections of data, only some are useful, whereas others are redundant. This redundancy decreases performance in terms of computing overhead, excessive transmission, and covering a large space. The process of selecting and analyzing the useful information from the collection of sensed data is called mining. Mining is used to produce more consistent, accurate, and useful information than that provided by any individual sensor node. Data mining has been widely applied in many areas, such as object recognition, wireless sensor networks (WSNs), image processing, environment mapping, and localization. Nowadays, Internet of Things utilizes WSN as a necessary platform for sensing and communication of the data. For efficiency, mining of spatial and temporal data is performed on the sensed sample collected by sensor nodes. Therefore, in this paper, a redundancy removal strategy is proposed, which performs mining on collected data to select the appropriate information before forwarding to a base station or a cluster head in the WSN. Extensive simulations were conducted, and the related results showed that the proposed scheme had better performance compared to other schemes in our simulated scenarios.
58 citations
TL;DR: In this article, the authors proposed a novel protocol that allows suppliers and grid operators to collect users' aggregate metering data in a secure and privacy-preserving manner using secure multiparty computation to ensure privacy protection.
Abstract: Smart grid allows fine-grained smart metering data collection which can improve the efficiency and reliability of the grid. Unfortunately, this vast collection of data also imposes risks to users’ privacy. In this paper, we propose a novel protocol that allows suppliers and grid operators to collect users’ aggregate metering data in a secure and privacy-preserving manner. We use secure multiparty computation to ensure privacy protection. In addition, we propose three different data aggregation algorithms that offer different balances between privacy-protection and performance. Our protocol is designed for a realistic scenario in which the data need to be sent to different parties, such as grid operators and suppliers. Furthermore, it facilitates an accurate calculation of transmission, distribution, and grid balancing fees in a privacy-preserving manner. We also present a security analysis and a performance evaluation of our protocol based on existing multiparty computation algorithms.
57 citations
TL;DR: This work considers distribution estimation over user-contributed data meanwhile providing rigid protection of their data with local $\epsilon$ε-differential privacy ($ε-LDP), which sanitizes each user's data on the client's side (e.g, on the user's mobile device).
Abstract: For the purpose of improving the quality of services, softwares or online services are collecting various of user data, such as personal information and locations. Such data facilitates mining statistical knowledge of users, but threatens users’ privacy as it may reveal sensitive information (e.g., identities and activities) about individuals. This work considers distribution estimation over user-contributed data meanwhile providing rigid protection of their data with local $\epsilon$e-differential privacy ($\epsilon$e-LDP), which sanitizes each user's data on the client's side (e.g, on the user's mobile device). Our privacy protection covers both qualitative data (e.g., categorical data) and discrete quantitative data (e.g., location data). Specifically, for categorical data, we derive an optimal $\epsilon$e-LDP mechanism (termed as $k$k-subset mechanism) from mutual information perspective, and further show its optimality over existing approaches within the context of discrete distribution estimation; for discrete quantitative data that have arbitrary distance metric, we provide an efficient extension of $k$k-subset mechanism by proposing a variant of the popular Exponential Mechanism (EM) to tackle the asymmetry issue on the data domain. Experiments on real-world datasets and simulated scenarios show that our mechanism is highly efficient and reduces nearly a fraction of $\exp (-\frac{\epsilon }{2})$exp(-e2) error for distribution estimation when compared to existing approaches.
56 citations
TL;DR: A novel efficient and location privacy-preserving data sharing scheme with collusion resistance with low data querying failure probability is proposed in IoV, which enables the collection and distribution of the data captured by vehicular sensors.
53 citations
TL;DR: This paper proposes to construct an aggregation tree (AT) for complex queries with the minimum communication cost by connecting a set of aggregation operations with maximum aggregation gain and formalizes the aggregation gain by jointly considering the data pruning power and aggregation cost.
Abstract: Data aggregation is a fundamental operation in Internet of Things (IoT) applications, e.g., distributed Internet-based industrial control and computing systems. As IoT devices are increasingly connected to the system via resource-constrained wireless communication links, it is critical to perform communication-efficient data aggregation to answer complex queries (e.g., skyline queries and equality joins) from IoT applications. In this paper, we investigate the problem of constructing an aggregation tree (AT) for complex queries with the minimum communication cost. As complex queries have a dynamic size of intermediate results, existing Steiner tree-based approaches for traditional query operators, e.g., MIN and top- ${k}$ , cannot be directly applied. We first formalize the aggregation gain by jointly considering the data pruning power (the size of data points that can be pruned during the aggregation for complex queries) and aggregation cost (the size of data points transmitted for the aggregation). By maximizing the aggregation gain, the data set that has a higher pruning power and a smaller size is selected and transferred for data aggregation at succeeding nodes. We then propose to construct the AT by connecting a set of aggregation operations with maximum aggregation gain. Extensive evaluation shows that our proposed framework achieves the promising results.
TL;DR: Wang et al. as mentioned in this paper proposed a secure consensus-based data aggregation algorithm that guarantees an accurate sum aggregation while preserving the privacy of sensitive data, and proved that the proposed algorithm converges accurately and is $(\epsilon, \sigma)$ -data privacy.
Abstract: Privacy-preserving data aggregation (DA) in ad hoc networks is a challenging problem, considering the distributed communication and control requirement, dynamic network topology, unreliable communication links, etc. Different from the widely used cryptographic approaches, in this paper, we address this challenging problem by exploiting the distributed consensus technique. We first propose a secure consensus-based DA algorithm that guarantees an accurate sum aggregation while preserving the privacy of sensitive data. Then, we prove that the proposed algorithm converges accurately and is $(\epsilon, \sigma)$ -data privacy, and the mathematical relationship between $\epsilon$ and $\sigma$ is provided. Extensive simulations have shown that the proposed algorithm has high accuracy and low complexity, and they are robust against network dynamics.
TL;DR: The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems, however, the sensory data provided by individual participants is usually not reliable and the results can be misleading.
Abstract: The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To better utilize such sensory data, the topic of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to address the privacy concerns of individual users. In this article, we propose a novel privacy-preserving truth discovery (PPTD) framework, which can protect not only users’ sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggregation on users’ encrypted data using a homomorphic cryptosystem, which can guarantee both high accuracy and strong privacy protection. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Additionally, we design an incremental PPTD scheme for the scenarios where the sensory data are collected in a streaming manner. Extensive experiments based on two real-world crowd sensing systems demonstrate that the proposed framework can generate accurate aggregated results while protecting users’ private information.
TL;DR: The security analysis indicates that the proposed scheme is proved to be secure in the random oracle model, satisfying all security and privacy requirements, and achieves lowest computation and communication costs, thus appropriate for practical applications.
Abstract: Smart grid, characterized by high efficiency, security, and flexibility, is gradually replacing the traditional power grid. Data aggregation technology is frequently used to avoid user privacy disclosure as a result of power consumption data transmission in the smart grid. However, traditional one-dimensional data aggregation schemes fail to meet the demands of fine-grained analysis. Therefore, this paper proposes an efficient privacy-preserving multi-dimensional data aggregation (P
2
MDA) scheme in smart grid by virtue of homomorphic encryption and superincreasing sequence. The security analysis indicates that the proposed scheme is proved to be secure in the random oracle model, satisfying all security and privacy requirements. The extensive performance analysis shows that in comparison to the related schemes, the proposed scheme achieves lowest computation and communication costs, thus appropriate for practical applications.
TL;DR: This paper presents a novel energy-efficient secure data aggregation scheme cluster-based private data aggregation (CSDA) based on cluster privacy-preserving that has good flexibility and practical applicability using the slice-assemble technology.
Abstract: With the development of wireless sensor networks, privacy-preserving has become a very important problem in numerous wireless sensor networks (WSN) applications. This paper presents a novel energy-efficient secure data aggregation scheme cluster-based private data aggregation (CSDA) based on cluster privacy-preserving. It has good flexibility and practical applicability using the slice-assemble technology. And, the number of fragments will dynamically change from the change of the network scale. Then, it can reduce communication overhead and energy consumption. Finally, the simulation results show that the proposed aggregation method demonstrates better performance in data aggregation precision, privacy-preserving and communication efficiency than other methods.
TL;DR: This work proposes Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT, which offers good performance in terms of network lifetime, delay, and packet delivery ratio.
Abstract: Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL’s performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.
TL;DR: A similarity-aware data aggregation using a fuzzy c-means approach for wireless sensor networks is proposed and can achieve better performance than traditional methods in terms of data outlier detection accuracy and relative recovery error.
Abstract: For resource-constrained IoT systems, data collection is one of the fundamental operations to reduce the energy dissipation of sensor nodes and improve the network lifetime. However, an anomaly or deviation will exert a great influence on the quality of data collected, especially for a data aggregation scheme. By taking into account data-aware clustering and detection of anomalous events, a similarity-aware data aggregation using a fuzzy c-means approach for wireless sensor networks is proposed. Firstly, by using a fuzzy c-means approach, the clustering process can be performed to organize sensors into clusters based on data similarity. Next, an effective support degree function is defined for further outlier diagnosis. Afterwards, the appropriate weight of valid data can be obtained by taking advantage of the probability distribution characteristics of normal samples within a certain period. Finally, the aggregation result in the cluster can be estimated. Practical database-based simulations have confirmed that the proposed data aggregation method can achieve better performance than traditional methods in terms of data outlier detection accuracy and relative recovery error.
TL;DR: In this paper, a distributed method is proposed to set child balance among nodes, and a dynamic data aggregation approach based on Learning Automata was proposed for Routing Protocol for Low-Power and Lossy Networks (LA-RPL).
Abstract: “Internet of Things (IoT)” has emerged as a novel concept in the world of technology and communication. In modern network technologies, the capability of transmitting data through data communication networks (such as Internet or intranet) is provided for each organism (e.g., human beings, animals, things, and so forth). Due to the limited hardware and operational communication capability as well as small dimensions, IoT undergoes several challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation, and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph increased through restricting the degree; and network congestion reduced as a result. In addition, a dynamic data aggregation approach based on Learning Automata was proposed for Routing Protocol for Low-Power and Lossy Networks (LA-RPL). More specifically, each node was equipped with learning automata in order to perform data aggregation and transmissions. Simulation and experimental results indicate that the LA-RPL has better efficiency than the basic methods used in terms of energy consumption, network control overhead, end-to-end delay, loss packet and aggregation rates.
TL;DR: A Two Level Data Aggregation (TLDA) Protocol for Prolonging the Lifetime of Periodic Sensor Networks is proposed and extensive simulation results are conducted using OMNeT++ network simulator and based on real data of sensor network to show the efficiency of the TLDA protocol compared with two existing methods.
Abstract: One big contributor in the future of the Internet of Things is the Periodic Sensor Networks (PSNs) because it has been used by many applications in real life. The main challenge in this type of networks is to gather the huge amount of data periodically in an energy saving way and then transmit them to the base station in order to extend the lifetime of PSN. Since the limited nature of the sensors batteries power, therefore, an energy-efficient data aggregation method is needed to optimize both energy and lifetime in PSNs. This article proposes a Two Level Data Aggregation (TLDA) Protocol for Prolonging the Lifetime of Periodic Sensor Networks. TLDA works in a periodic way. Each period consists of two data aggregation levels. The first level of data aggregation is applied at the sensor node. This level includes data collection, the sliding window to generate a varying number of segments with different lengths, and data aggregation using Adaptive Piecewise Constant Approximation technique to reduce the amount of data collected by each sensor. The second level is applied at the aggregator. It includes grouping received data sets based on the chaining hash table with SAX quantization method, finding and lowering the duplicate sets, finding and merging the duplicate readings, and transmit the aggregated data to the sink. Extensive simulation results are conducted using OMNeT++ network simulator and based on real data of sensor network to show the efficiency of the TLDA protocol compared with two existing methods.
TL;DR: A novel data aggregation scheme is proposed which is based on self-organized map neural network to reduce redundant data and eliminate outliers, and cosine similarity is used to improve the clustering process of sensor nodes based on the density and similarity of the data.
Abstract: Wireless sensor network allows efficient data collection and transmission in IoT environment. Since it usually consists of a large number of sensor nodes, a significant amount of redundant data and outliers are generated which deteriorate the network performance. In this paper, a novel data aggregation scheme is proposed which is based on self-organized map neural network to reduce redundant data and eliminate outliers. In addition, cosine similarity is used to improve the clustering process of sensor nodes based on the density and similarity of the data, and interquartile analysis is adopted to remove outliers. It allows to significantly reduce the energy consumption and enhance the network performance. Extensive simulation with real dataset shows that the proposed scheme consistently outperforms the existing representative data aggregation schemes in term of data reduction rate, network lifetime, and energy efficiency.
01 Mar 2019
TL;DR: This study proposes a distributed, privacy-preserving, and secure meter data aggregation framework, backed up by Blockchain and homomorphic encryption (HE) technologies.
Abstract: A significant progress in modern power grids is witnessed by the tendency of becoming complex cyber-physical systems. As a fundamental physical infrastructure, smart meter in the demand side provides real-time energy consumption information to the utility. However, ensuring information security and privacy in the meter data aggregation process is a non-trivial task. This study proposes a distributed, privacy-preserving, and secure meter data aggregation framework, backed up by Blockchain and homomorphic encryption (HE) technologies. Meter data are aggregated and verified by a hierarchical Blockchain system, in which the consensus mechanism is supported by the practical Byzantine fault tolerance algorithm. On the top of the Blockchain system, HE technology is used to protect the privacy of individual meter data items during the aggregation process. Performance analysis is conducted to validate the proposed method.
TL;DR: This paper proposes a two-phase framework that computes the average value while preserving heterogeneous privacy for nodes’ private data through one-shot noise perturbation and obtains the closed-form expression of computation accuracy.
Abstract: Collaborative computing uses multiple data servers to jointly complete data analysis, e.g., statistical analysis and inference. One major obstruction for it lies in privacy concern, which is directly associated with nodes’ participation and the fidelity of received data. Existing privacy-preserving paradigms for cloud computing and distributed data aggregation only provide nodes with homogeneous privacy protection without consideration of nodes’ diverse trust degrees to different data servers. We propose a two-phase framework that computes the average value while preserving heterogeneous privacy for nodes’ private data. The new challenge is that in the premise of meeting privacy requirements, we should guarantee the proposed framework has the same computation accuracy with existing privacy-aware solutions. In this paper, nodes obtain heterogeneous privacy protection in the face of different data servers through one-shot noise perturbation. Based on the definition of KL privacy, we derive the analytical expressions of the privacy preserving degrees (PPDs) and quantify the relation between different PPDs. Then, we obtain the closed-form expression of computation accuracy. Furthermore, an efficient incentive mechanism is proposed to achieve optimized computation accuracy when data servers have fixed budgets. Finally, extensive simulations are conducted to verify the obtained theoretical results.
01 Jan 2019
TL;DR: This chapter introduces a secure data processing and transmission schema in WSN and proposes and applies an evaluation criteria for the existing secure clustering algorithms.
Abstract: Building a secure routing protocol in WSN is not trivial process. Thee are two main types of security attacks against WSNs: active and passive. WSN as a new category of computer-based computing platforms and network structures is showing new applications in different areas such as environmental monitoring, health care and military applications. Although there are a lot of secure data transmission schemes designed for data aggregation and transmission over a network, the limited resources and the complex environment make it invisible to be used with WSNs. Furthermore, secure data transmission is a big challenging issue in WSNs especially for the application that uses image as its main data such as military applications. This problem is mainly related to the limited resources and data processing capabilities. This chapter introduces a secure data processing and transmission schema in WSN. The chapter reviewed and critically discussed the most prominent secure clustering routing algorithms that have been developed for WSNs. Then, we explained the guidelines and the steps towards building a simple solution for securing the dynamic cluster network while consuming as little energy as possible and is adapted to a low computing power. Moreover, four phased towards building a secure clustering algorithm for WSN are proposed. These phases are secure cluster head selection, secure cluster formation, secure data aggregation by the cluster head from its cluster nodes, and secure data routing to the base station. Also, the chapter proposes and applies an evaluation criteria for the existing secure clustering algorithms.
TL;DR: Evaluation results demonstrate that CG-E2S2 with the optimal policy outperforms the comparison schemes in terms of energy efficiency, data traffic volume, and data consistency.
17 Jun 2019
TL;DR: Basic privacy building blocks are covered, including differential privacy and homomorphic encryption, and privacy solutions specific to three different types of data use that are relevant for edge-based applications are discussed: data aggregation techniques, point-of-interest (POI) services and traffic information services, and crowdsourcing.
Abstract: In an edge-enabled data management and computing environment, it is critical to ensure the privacy of the information acquired, processed, and exchanged among the different parties. The problem is complex because of the large scale, mobility, device, and protocol heterogeneity. Also, unlike in conventional environments, communication may be fragmented and portions of the environment can be physically unprotected. To date, there are several privacy-enhancing techniques, such as secure multiparty computation techniques, private information retrieval (PIR), and data sanitization techniques. However, there is not a single technique that works for all possible uses of the data in edge systems. In addition, these techniques are computationally expensive and thus may not be suitable for edge devices. In this paper, we first cover basic privacy building blocks, including differential privacy and homomorphic encryption. We then discuss privacy solutions specific to three different types of data use that are relevant for edge-based applications: data aggregation techniques, point-of-interest (POI) services and traffic information services, and crowdsourcing. These applications have been selected as they provide a broad spectrum of edge computing applications. Throughout this paper, we outline open research directions.
TL;DR: In this survey the various existing solutions in WSN are surveyed and an attempt is made to classify them based on the node topology and mechanisms employed for assuring privacy.
Abstract: Wireless sensor networks (WSN) are made up of energy constraint tiny sensing devices which are distributed geographically to monitor inhabited remote areas by collecting the physical phenomenon like temperature, pressure etc. They play a vital role in military surveillance, environment monitoring etc. Unstructured topology in WSN results in large amount of redundant data being transmitted over the resource constraint devices which leads to energy starvation problem. Since the nodes are prone to tamper, thanks to their environment, ensuring the privacy of sensitive data being aggregated and transmitted is important. Hence data aggregation schemes which minimize the data redundancy with the guarantee of security become the attraction of research. Many secured aggregation schemes have been proposed by researchers. In this survey the various existing solutions are surveyed and an attempt is made to classify them based on the node topology and mechanisms employed for assuring privacy.
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TL;DR: In this article, a new paradigm, Data Capsule, is presented for automatic compliance checking of data privacy regulations in heterogeneous data processing infrastructures, where a data subject's data is paired with a policy governing how the data is processed.
Abstract: The increasing pace of data collection has led to increasing awareness of privacy risks, resulting in new data privacy regulations like General data Protection Regulation (GDPR). Such regulations are an important step, but automatic compliance checking is challenging. In this work, we present a new paradigm, Data Capsule, for automatic compliance checking of data privacy regulations in heterogeneous data processing infrastructures. Our key insight is to pair up a data subject's data with a policy governing how the data is processed. Specified in our formal policy language: PrivPolicy, the policy is created and provided by the data subject alongside the data, and is associated with the data throughout the life-cycle of data processing (e.g., data transformation by data processing systems, data aggregation of multiple data subjects' data). We introduce a solution for static enforcement of privacy policies based on the concept of residual policies, and present a novel algorithm based on abstract interpretation for deriving residual policies in PrivPolicy. Our solution ensures compliance automatically, and is designed for deployment alongside existing infrastructure. We also design and develop PrivGuard, a reference data capsule manager that implements all the functionalities of Data Capsule paradigm.
24 Jun 2019
TL;DR: Results demonstrate that the proposed Integrated Divide and Conquer with Enhanced K-means technique (IDiCoEK) technique can save energy by decreasing the measures sent to the sink whilst conserving a suitable level of data accuracy at the sink node.
Abstract: In the Internet of Things (IoTs) future, the Wireless Sensor Networks (WSNs) represent one of the big data contributors due to the wide range of real-life applications that use this type of networks. The data volume increases in unexpected ratio. The dense WSN can lead to an increase in the redundant data in the gathered measures of the sensor node. Therefore, it is essential to apply energy-efficient data aggregation to remove the data redundancy and maintain a suitable rate of accuracy. This paper proposes an Integrated Divide and Conquer with Enhanced K-means technique (IDiCoEK) for energy-saving data aggregation in WSNs. The IDiCoEK aggregates the measures in two levels: the node and cluster head levels. A divide and conquer algorithm is applied at the sensor node to remove the redundant data from the collected measures and then send it to the cluster head. The cluster head applies an enhanced K-means approach for clustering the received data sets from the sensor nodes into groups of near similar sets and then the best representative set will be sent to the base station from each group. The IDiCoEK performance is assessed using OMNeT++ network simulator with real data readings of sensor nodes. Results demonstrate that our IDiCoEK technique can save energy by decreasing the measures sent to the sink whilst conserving a suitable level of data accuracy at the sink node.
TL;DR: For most WSN applications, since the underlying physical layer prefers frames with small payloads, the proposed TADA utilizes less transmissions from sensors to UAV in each round of data aggregation, and thus can yields a lower energy consumption.
TL;DR: An efficient data aggregation scheduling algorithm called EDAS is proposed, which exploits the fewest-children-first rule to choose the forwarding nodes to benefit the link scheduling and a novel algorithm called NDAS by making full use of the characteristics of multi-channel asynchronous duty-cycled WSNs is proposed.
Abstract: Data aggregation scheduling is a critical issue in WSNs. This paper studies the Delay efficient Data Aggregation scheduling problem in multi-Channel asynchronous Duty-cycled WSNs (DDACD problem), which aims to accomplish data aggregation with minimum delay. Existing studies, nevertheless, either focus on non-sleeping scenarios or assume that nodes communicate with one single channel, and thus may have poor performance if directly applied to multi-channel asynchronous duty-cycled scenarios. We first show that the DDACD problem is NP-hard. Then, we propose two new concepts of candidate active conflict graphs (CACGs) and feasible active conflict graphs (FACGs) to depict the relationship of the data aggregation links and present two coloring methods to well separate the links at different time slots or on different channels. Based on these two new concepts and two coloring methods, we propose an efficient data aggregation scheduling algorithm called EDAS, which exploits the fewest-children-first rule to choose the forwarding nodes to benefit the link scheduling. To reduce unused time slots or channels, we further propose a novel algorithm called NDAS by making full use of the characteristics of multi-channel asynchronous duty-cycled WSNs. We prove that our algorithms can achieve provable performance guarantee. The results of extensive simulations confirm the efficiency of our algorithms.