Showing papers on "Data aggregator published in 2017"

A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT

Abstract: Fog computing-enhanced Internet of Things (IoT) has recently received considerable attention, as the fog devices deployed at the network edge can not only provide low latency, location awareness but also improve real-time and quality of services in IoT application scenarios. Privacy-preserving data aggregation is one of typical fog computing applications in IoT, and many privacy-preserving data aggregation schemes have been proposed in the past years. However, most of them only support data aggregation for homogeneous IoT devices, and cannot aggregate hybrid IoT devices’ data into one in some real IoT applications. To address this challenge, in this paper, we present a lightweight privacy-preserving data aggregation scheme, called Lightweight Privacy-preserving Data Aggregation, for fog computing-enhanced IoT. The proposed LPDA is characterized by employing the homomorphic Paillier encryption, Chinese Remainder Theorem, and one-way hash chain techniques to not only aggregate hybrid IoT devices’ data into one, but also early filter injected false data at the network edge. Detailed security analysis shows LPDA is really secure and privacy-enhanced with differential privacy techniques. In addition, extensive performance evaluations are conducted, and the results indicate LPDA is really lightweight in fog computing-enhanced IoT.

Efficient and Privacy-Preserving Data Aggregation Scheme for Smart Grid Against Internal Adversaries

Abstract: Privacy-preserving data aggregation (P2DA) is an important basic building block that can protect consumer’s privacy in the smart grid environment because it could be used to prevent the extraction of the electricity consumption information of a specific consumer. Due to this important function, the P2DA scheme for the smart grid has attracted a lot of attention from both academic and industry researchers who have proposed many P2DA schemes for the smart grid in recent years. However, most of these P2DA schemes are not secure against internal attackers or cannot provide data integrity. Besides, their computation costs are not satisfactory because the bilinear pairing operation or the hash-to-point operation is performed at the smart meter’s side. To address the deficiencies of previous schemes, we propose a new P2DA scheme against internal attackers using Boneh–Goh–Nissim public key cryptography. The proposed P2DA scheme does not use bilinear pairing or hash-to-point operation making it be more computationally efficient than previous P2DA schemes. We also show that the proposed P2DA scheme is provably secure and can meet various security requirements.

Recursive Principal Component Analysis-Based Data Outlier Detection and Sensor Data Aggregation in IoT Systems

Abstract: Internet of Things (IoT) is emerging as the underlying technology of our connected society, which enables many advanced applications. In IoT-enabled applications, information of application surroundings is gathered by networked sensors, especially wireless sensors due to their advantage of infrastructure-free deployment. However, the pervasive deployment of wireless sensor nodes generate massive amount of sensor data, and data outliers are frequently incurred due to the dynamic nature of wireless channels. As operation of IoT systems relies on sensor data, data redundancy and data outliers could significantly reduce the effectiveness of IoT applications or even mislead systems into unsafe conditions. In this paper, a cluster-based data analysis framework is proposed using recursive principal component analysis (R-PCA), which can aggregate the redundant data and detect the outliers in the meantime. More specifically, at a cluster head, spatially correlated sensor data collected from cluster members are aggregated by extracting the principal components (PCs), and potential data outliers are determined by the abnormal squared prediction error score, which is defined as the square of residual value after extraction of PCs. With R-PCA, the parameters of PCA model can be recursively updated to adapt to the changes in IoT systems. Cluster-based data analysis framework also releases the computational and processing burdens on sensor nodes. Practical databases-based simulations have confirmed that the proposed framework efficiently aggregates the correlated sensor data with high recovery accuracy. The data outlier detection accuracy is also improved by the proposed method compared to other existing algorithms.

Data Aggregation in Wireless Sensor Networks: Previous Research, Current Status and Future Directions

Abstract: Wireless sensor networks (WSNs) consist of large number of small sized sensor nodes, whose main task is to sense the desired phenomena in a particular region of interest. These networks have large number of applications such as habitat monitoring, disaster management, security and military etc. Sensor nodes are very small in size and have limited processing capability as these nodes have very low battery power. WSNs are also prone to failure, due to low battery power constraint. Data aggregation is an energy efficient technique in WSNs. Due to high node density in sensor networks same data is sensed by many nodes, which results in redundancy. This redundancy can be eliminated by using data aggregation approach while routing packets from source nodes to base station. Researchers still face trouble to select an efficient and appropriate data aggregation technique from the existing literature of WSNs. This research work depicts a broad methodical literature analysis of data aggregation in the area of WSNs in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 123 research papers out of large collection of 932 research papers published in 20 foremost workshops, symposiums, conferences and 17 prominent journals. The current status of data aggregation in WSNs is distributed into various categories. Methodical analysis of data aggregation in WSNs is presented which includes techniques, tools, methodology and challenges in data aggregation. The literature covered fifteen types of data aggregation techniques in WSNs. Detailed analysis of this research work will help researchers to find the important characteristics of data aggregation techniques and will also help to select the most suitable technique for data aggregation. Research issues and future research directions have also been suggested in this research literature.

Efficient Privacy-Preserving Cube-Data Aggregation Scheme for Smart Grids

Abstract: Efficient power management in smart grids requires obtaining power consumption data from each resident. However, data concerning user’s electricity consumption might reveal sensitive information, such as living habits and lifestyles. In order to solve this problem, this paper proposes a privacy-preserving cube-data aggregation scheme for electricity consumption. In our scheme, a data item is described as a multi-dimensional data structure ( $l$ -dimensional), and users form and live in multiple residential areas ( $m$ areas, and at most $n$ users in each area). Based on Horner’s Rule, for each user, we construct a user-level polynomial to store dimensional values in a single data space by using the first Horner parameter. After embedding the second Horner parameter into the polynomial, the polynomial is hidden by using Paillier cryptosystem. By aggregating data from $m$ areas, we hide the area-level polynomial into the final output. Moreover, we propose a batch verification scheme in multi-dimensional data to reduce authentication cost. Finally, our analysis shows that the proposed scheme is efficient in terms of computation and communication costs, suitable for massive user groups, and supports the flexible and rapid growth of residential scales in smart grids.

A Secure Privacy-Preserving Data Aggregation Scheme Based on Bilinear ElGamal Cryptosystem for Remote Health Monitoring Systems

Abstract: Due to advancements in the development of wireless medical sensing devices and wireless communication technologies, the wireless body area network (WBAN) has become an eminent part of e-healthcare systems. WBAN uses medical sensors to continuously monitor and collect the physiological parameters of a patient’s health and send them to a remote medical server through a portable digital assistance (PDA)/mobile. Due to limitations in communication, such as power, storage, and the computational capabilities of sensors, data aggregation techniques are used to reduce the communication overhead in real-time data transmission in WBAN. However, since the WBAN transmits sensitive health data, data security and data privacy are a major concern. In this paper, we propose a secure privacy-preserving data aggregation (SPPDA) scheme based on bilinear pairing for remote health monitoring systems to improve data aggregation efficiency and data privacy. Our proposed SPPDA scheme utilizes the homomorphic property of the bilinear ElGamal cryptosystem to perform privacy-preserving secure computation and combines it with the aggregate signature scheme, enabling data authenticity/integrity in the WBAN. The proposed SPPDA scheme is proved to be semantically secure under the decisional bilinear Diffie–Hellman assumption. Security analysis demonstrates that our proposed scheme preserves data confidentiality, data authenticity, and data privacy; it also resists passive eavesdropping and replay attacks. A performance evaluation based on simulation results and a comparison of computational cost with related schemes show that data aggregation and batch verification at the PDA significantly reduce communication and transmission overhead and support efficient computation at the remote server.

Massive Machine Type Communication With Data Aggregation and Resource Scheduling

Abstract: To enable massive machine type communication (mMTC), data aggregation is a promising approach to reduce the congestion caused by a massive number of machine type devices (MTDs). In this paper, we consider a two-phase cellular-based mMTC network, where MTDs transmit to aggregators (i.e., aggregation phase) and the aggregated data is then relayed to base stations (i.e., relaying phase). Due to the limited resources, the aggregators not only aggregate data, but also schedule resources among MTDs. We consider two scheduling schemes: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). By leveraging the stochastic geometry, we present a tractable analytical framework to investigate the signal-to-interference ratio (SIR) for each phase, thereby computing the MTD success probability, the average number of successful MTDs and probability of successful channel utilization, which are the key metrics characterizing the overall mMTC performance. Our numerical results show that, although the CRS outperforms the RRS in terms of SIR at the aggregation phase, the simpler RRS has almost the same performance as the CRS for most of the cases with regards to the overall mMTC performance. Furthermore, the provision of more resources at the aggregation phase is not always beneficial to the mMTC performance.

Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing

Abstract: Mobile crowdsensing, which collects environmental information from mobile phone users, is growing in popularity. These data can be used by companies for marketing surveys or decision making. However, collecting sensing data from other users may violate their privacy. Moreover, the data aggregator and/or the participants of crowdsensing may be untrusted entities. Recent studies have proposed randomized response schemes for anonymized data collection. This kind of data collection can analyze the sensing data of users statistically without precise information about other users' sensing results. However, traditional randomized response schemes and their extensions require a large number of samples to achieve proper estimation. In this paper, we propose a new anonymized data-collection scheme that can estimate data distributions more accurately. Using simulations with synthetic and real datasets, we prove that our proposed method can reduce the mean squared error and the JS divergence by more than 85% as compared with other existing studies.

Comparison of Different Data Aggregation Techniques in Distributed Sensor Networks

Abstract: Wireless sensor networks (WSNs) are almost everywhere, they are exploited for thousands of applications in a densely distributed manner. Such deployment makes WSNs one of the highly anticipated key contributors of the big data nowadays. Hence, data aggregation is attracting much attention from researchers as efficient way to reduce the huge volume of data generated in WSNs by eliminating the redundancy among sensing data. In this paper, we propose an efficient data aggregation technique for clustering-based periodic wireless sensor networks. Further to a local aggregation at sensor node level, our technique allows cluster-head to eliminate redundant data sets generated by neighbouring nodes by applying three data aggregation methods. These proposed methods are based on the sets similarity functions, the one-way Anova model with statistical tests and the distance functions, respectively. Based on real sensor data, we have analyed their performances according to the energy consumption and the data latency and accuracy, and we show how these methods can significantly improve the performance of sensor networks.

A Distance-Based Data Aggregation Technique for Periodic Sensor Networks

Abstract: Monitoring phenomena and environments is an emergent and required field in our today systems and applications Hence, wireless sensor networks (WSNs) have attracted considerable attention from the research community as an efficient way to explore various kinds of environments Sensor networks applications can be useful in different domains (terrestrial, underwater, space exploration, etc) However, one of the major constraints in such networks is the energy consumption that increases when data transmission increases Consequently, optimizing data transmission is one of the most significant criteria in WSNs that can conserve energy of sensors and extend network lifetime In this article, we propose an efficient data transmission protocol that consists in two phases of data aggregation Our proposed protocol searches, in the first phase, similarities between measures collected by each sensor In the second phase, it uses distance-based functions to find similarity between sets of collected data The main goal of these phases is to reduce the data transmitted from both sensors and cluster-heads (CHs) in a clustering-based scheme network To evaluate the performance of the proposed protocol, experiments on real sensor data from both terrestrial and underwater networks have been conducted Compared to other existing techniques, simulation and real experimentations show that our protocol can be effectively used to reduce data transmission and increase network lifetime, while still keeping data integrity of the collected data

A Distributed Delay-Efficient Data Aggregation Scheduling for Duty-Cycled WSNs

Abstract: With the growing interest in wireless sensor networks (WSNs), minimizing network delay and maximizing sensor (node) lifetime are important challenges. Since the sensor battery is one of the most precious resources in a WSN, efficient utilization of the energy to prolong the network lifetime has been the focus of much of the research on WSNs. For that reason, many previous research efforts have tried to achieve tradeoffs in terms of network delay and energy cost for such data aggregation tasks. Recently, duty-cycling technique, i.e., periodically switching ON and OFF communication and sensing capabilities, has been considered to significantly reduce the active time of sensor nodes and thus extend network lifetime. However, this technique causes challenges for data aggregation. In this paper, we present a distributed approach, named distributed delay efficient data aggregation scheduling (DEDAS-D) to solve the aggregation-scheduling problem in duty-cycled WSNs. The analysis indicates that our solution is a better approach to solve this problem. We conduct extensive simulations to corroborate our analysis and show that DEDAS-D outperforms other distributed schemes and achieves an asymptotic performance compared with centralized scheme in terms of data aggregation delay.

Distributed Data Service for Data Management in Internet of Things Middleware.

Abstract: The development of the Internet of Things (IoT) is closely related to a considerable increase in the number and variety of devices connected to the Internet. Sensors have become a regular component of our environment, as well as smart phones and other devices that continuously collect data about our lives even without our intervention. With such connected devices, a broad range of applications has been developed and deployed, including those dealing with massive volumes of data. In this paper, we introduce a Distributed Data Service (DDS) to collect and process data for IoT environments. One central goal of this DDS is to enable multiple and distinct IoT middleware systems to share common data services from a loosely-coupled provider. In this context, we propose a new specification of functionalities for a DDS and the conception of the corresponding techniques for collecting, filtering and storing data conveniently and efficiently in this environment. Another contribution is a data aggregation component that is proposed to support efficient real-time data querying. To validate its data collecting and querying functionalities and performance, the proposed DDS is evaluated in two case studies regarding a simulated smart home system, the first case devoted to evaluating data collection and aggregation when the DDS is interacting with the UIoT middleware, and the second aimed at comparing the DDS data collection with this same functionality implemented within the Kaa middleware.

Distributed non-structure based data aggregation for duty-cycle wireless sensor networks

Abstract: Data aggregation is an essential operation for the sink to obtain summary information in a Wireless Sensor Network (WSN). The problem of Minimum Latency Aggregation Schedule (MLAS) which seeks a fastest and collision-free aggregation schedule has been well studied when nodes are always awake. However, in duty-cycle WSNs, nodes can only receive data in active state. In such networks, it is of great importance to exploit the limited active time slots to reduce aggregation latency. Unfortunately, few studies have addressed this issue and most previous aggregation methods rely on fixed structures which greatly limit the exploitation of the active time slots from other neighbors. In this paper, we investigate the MLAS problem in duty-cycle WSNs without considering structures. We propose the first distributed aggregation algorithm for duty-cycle WSNs, in which the aggregation tree and a conflict-free schedule are generated simultaneously. Compared with the previous centralized and distributed methods, the aggregation latency and the utilization ratio of available time slots are greatly improved. The theoretical analysis and simulation results verify that the proposed algorithm has high performance in terms of latency and communication cost.

Privacy-Preserving Verifiable Set Operation in Big Data for Cloud-Assisted Mobile Crowdsourcing

Abstract: The ubiquity of smartphones makes the mobile crowdsourcing possible, where the requester (task owner) can crowdsource data from the workers (smartphone users) by using their sensor-rich mobile devices. However, data collection, data aggregation, and data analysis have become challenging problems for a resource constrained requester when data volume is extremely large, i.e., big data. In particular to data analysis, set operations, including intersection, union, and complementation, exist in most big data analysis for filtering redundant data and preprocessing raw data. Facing challenges in terms of limited computation and storage resources, cloud-assisted approaches may serve as a promising way to tackle the big data analysis issue. However, workers may not be willing to participate if the privacy of their sensing data and identity are not well preserved in the untrusted cloud. In this paper, we propose to the use cloud to compute a set operation for the requester, at the same time workers’ data privacy and identities privacy are well preserved. Besides, the requester can verify the correctness of set operation results. We also extend our scheme to support data preprocessing, with which invalid data can be excluded before data analysis. By using batch verification and data update methods, the proposed scheme greatly reduces the computational cost. Extensive performance analysis and experiment based on real cloud system have shown both the feasibility and efficiency of our proposed scheme.

Local private ordinal data distribution estimation

Abstract: The categorical data that have natural ordering between categories are termed ordinal data, which are pervasive in numerous areas, including discrete sensor readings, metering data or preference options. Though aggregating such ordinal data from the population is facilitating plenty of crowdsourcing applications, contributing such data is privacy risky and may reveal sensitive information (e.g. locations, identities) about individuals. This work studies ordinal data aggregation for distribution estimation meanwhile locally preserving individuals' data privacy (such as on their mobile devices). Under e-geo-indistinguishable constraints, which capture intrinsic dissimilarity between ordinal categories in the framework of differential privacy, we provide an efficient and effective locally private mechanism: Subset Exponential Mechanism (SEM) for ordinal data distribution estimation. The mechanism randomly responds with a fixed-size subset of the categories with calibrated probability assignment. Specially for uniform ordinal data, we propose a circling technique to symmetrically randomizing categories and estimating frequencies of categories, hence the computational/space costs and estimation performance of SEM are further optimized. Besides contributing theoretical error bounds of SEM, we also evaluate the mechanism on extensive scenarios, the evaluation results show that SEM reduces distribution estimation error on average by exp(∊/2) factor over existing private mechanisms.

Improved Data Aggregation for Cluster Based Underwater Wireless Sensor Networks

Abstract: While performing underwater monitoring tasks, energy of the sensor nodes in Underwater Wireless Sensor Networks (UWSNs) vanishes continuously all over the network. The performance of Under Water (UW) sensor nodes mainly depends on their battery which is difficult to replace and therefore, energy saving becomes the main objective for increasing the lifespan of such network. The combination of clustering and data aggregation may be used to save energy. To further reduce power consumption during data aggregation at the Cluster Head, efficient scheduling techniques for data transmission are required by the cluster members. In this paper, an Improved Data Aggregation technique for Cluster Based UWSN is proposed where an efficient sleep-wake up algorithm is used for aggregating the sensed data and TDMA based transmission schedule is used to avoid intra and inter cluster collisions. Improvement in well-known existing protocols is achieved by the combination of data aggregation and data scheduling along with data fusion to minimize the energy consumption. The performance of the proposed scheme is evaluated by comparing with existing protocols and results have shown better performance of the proposed scheme than the existing approaches in terms of packet drop, end-to-end delay, and energy consumption. The proposed technique also reduces the number of transmissions and efficiently utilizes the UW sensor nodes.

Private data aggregation with integrity assurance and fault tolerance for mobile crowd-sensing

Abstract: Mobile crowd-sensing can learn the aggregate statistics over personal data to produce useful knowledge about the world. Since personal data may be privacy-sensitive, the aggregator should only gain desired statistics without learning anything about the personal data. To guarantee differential privacy of personal data under an untrusted aggregator, existing approaches encrypt the noisy personal data, and allow the aggregator to get a noisy sum. However, these approaches lack of either efficient support of dynamic joins and leaves, or secure data-integrity verification, or fault tolerance. In this paper, we propose a novel private data aggregation scheme to address these issues for mobile crowd-sensing applications. In our scheme, we first design an efficient group management protocol to deal with the participants' dynamic joins and leaves. Then we enhance the scheme with data-integrity verification by considering the security vulnerability of limited data range. Moreover, we guarantee fault tolerance by leveraging a future message buffering mechanism, enabling continuously obtaining aggregate results and integrity verifications when failures happen. The analysis indicates that our scheme achieves desired properties, and the performance evaluation demonstrates the scheme's efficiency in terms of communication and computation overhead.

Enabling Privacy in a Distributed Game-Theoretical Scheduling System for Domestic Appliances

Abstract: Demand side management (DSM) makes it possible to adjust the load experienced by the power grid while reducing the consumers’ bill. Game-theoretic DSM is an appealing decentralized approach for collaboratively scheduling the usage of domestic electrical appliances within a set of households while meeting the users’ preferences about the usage time. The drawback of distributed DSM protocols is that they require each user to communicate his/her own energy consumption patterns, which may leak sensitive information regarding private habits. This paper proposes a distributed privacy-friendly DSM system that preserves users’ privacy by integrating data aggregation and perturbation techniques: users decide their schedule according to aggregated consumption measurements perturbed by means of additive white Gaussian noise. We evaluate the noise power and the number of users required to achieve a given privacy level, quantified by means of the increase of the information entropy of the aggregated energy consumption pattern. The performance of our proposed DSM system is compared to the one of a benchmark system that does not support privacy preservation in terms of total bill, peak demand, and convergence time. Results show that privacy can be improved at the cost of increasing the peak demand and the number of game iterations, whereas the total bill is only marginally incremented.

Lightweight Data Aggregation Scheme against Internal Attackers in Smart Grid Using Elliptic Curve Cryptography

Abstract: Recent advances of Internet and microelectronics technologies have led to the concept of smart grid which has been a widespread concern for industry, governments, and academia. The openness of communications in the smart grid environment makes the system vulnerable to different types of attacks. The implementation of secure communication and the protection of consumers’ privacy have become challenging issues. The data aggregation scheme is an important technique for preserving consumers’ privacy because it can stop the leakage of a specific consumer’s data. To satisfy the security requirements of practical applications, a lot of data aggregation schemes were presented over the last several years. However, most of them suffer from security weaknesses or have poor performances. To reduce computation cost and achieve better security, we construct a lightweight data aggregation scheme against internal attackers in the smart grid environment using Elliptic Curve Cryptography (ECC). Security analysis of our proposed approach shows that it is provably secure and can provide confidentiality, authentication, and integrity. Performance analysis of the proposed scheme demonstrates that both computation and communication costs of the proposed scheme are much lower than the three previous schemes. As a result of these aforementioned benefits, the proposed lightweight data aggregation scheme is more practical for deployment in the smart grid environment.

A lightweight data aggregation scheme achieving privacy preservation and data integrity with differential privacy and fault tolerance

Abstract: To design an efficient and secure data aggregation scheme fitting real applications has been pursued by research communities for a long time. In this paper, we propose a novel secure data aggregation scheme to simultaneously achieve privacy preservation and data integrity with differential privacy and fault tolerance. Specifically, by introducing some auxiliary ciphertext subtly, a novel distributed solution for fault tolerant data aggregation is put forward to be able to aggregate the functioning smart meter measurements flexibly and efficiently for any rational number of malfunctioning smart meters with discretional long failure period. The proposed scheme also achieves a good tradeoff of accuracy and security of differential privacy for arbitrary number of malfunctioning smart meters. In the proposed scheme, a novel efficient authentication mechanism is also proposed to generate and share session keys in a noninteractive way, which is leveraged for AES encryption to achieve source authentication and data integrity of the transmitted data. Furthermore, through decentralizing the computational overhead and the authority of the hub-like entity of the gateway, the security of our proposed scheme is enhanced and the efficiency is improved significantly. Finally, extensive performance evaluations are conducted to illustrate that the proposed data aggregation scheme outperforms the state-of-the-art similar schemes in terms of computation complexity, communication cost, robustness of fault tolerance, and utility of differential privacy.

Optimized Clustering Protocol for Balancing Energy in Wireless Sensor Networks

Abstract: While wireless sensor networks (WSNs) are increasingly equipped to handle more complex functions and in-network processing may require these battery powered sensors to judiciously use their constrained energy to prolong the effective network lifetime. Cluster-based Hierarchical Routing Protocol using compressive sensing (CS) theory (CBHRP-CS) divides the network into several clusters, each managed by a set of CHs called a header. Each member of the header compresses the collected data using CS. This paper proposes an optimized clustering protocol using CS (OCP-CS) to improve the performance of WSNs by exploiting compressibility. In OCP-CS, each cluster is managed by a cluster head (CH). CHs are selected based on node concentration and sensor residual energy, and performs data aggregation using CS to reduce the energy consumed in the process of data sampling and transmission. Simulations show that our proposed protocol is effective in prolonging the network lifetime and supporting scalable data aggregation than existing protocols.

Privacy-Preserving Selective Aggregation of Online User Behavior Data

Abstract: Tons of online user behavior data are being generated every day on the booming and ubiquitous Internet. Growing efforts have been devoted to mining the abundant behavior data to extract valuable information for research purposes or business interests. However, online users’ privacy is thus under the risk of being exposed to third-parties. The last decade has witnessed a body of research works trying to perform data aggregation in a privacy-preserving way. Most of existing methods guarantee strong privacy protection yet at the cost of very limited aggregation operations, such as allowing only summation, which hardly satisfies the need of behavior analysis. In this paper, we propose a scheme PPSA, which encrypts users’ sensitive data to prevent privacy disclosure from both outside analysts and the aggregation service provider, and fully supports selective aggregate functions for online user behavior analysis while guaranteeing differential privacy. We have implemented our method and evaluated its performance using a trace-driven evaluation based on a real online behavior dataset. Experiment results show that our scheme effectively supports both overall aggregate queries and various selective aggregate queries with acceptable computation and communication overheads.

Preserving Data Integrity in IoT Networks Under Opportunistic Data Manipulation

Abstract: As Internet of Things (IoT) and Cyber-Physical systems become more ubiquitous and an integral part of our daily lives, it is important that we are able to trust the data aggregate from such systems. However, the interpretation of trustworthiness is contextual and varies according to the risk tolerance attitude of the concerned application and varying levels of uncertainty associated with the evidence upon which trust models act. Hence, the data integrity scoring mechanisms should have provisions to adapt to varying risk attitudes and uncertainties. In this paper, we propose a Bayesian inference model and a prospect theoretic framework for data integrity scoring that quantify the trustworthiness of data collected from IoT devices by a hub in the presence of an adversary manipulating data and an imperfect anomaly monitoring mechanism. The monitoring mechanism monitors the data being sent from each device and classifies the outcome as not compromised, compromised, and cannot be inferred. These outcomes are conceptualized as a multinomial hypothesis of a Bayesian inference model with three parameters which are then used for calculating a utility value on how reliable the aggregate data is. We use prospect theory inspired approach to quantify this data integrity score and evaluate trustworthiness of the aggregate data from the IoT framework. As decisions are based on how the data is fused, we propose two measuring models-one optimistic and another conservative. The proposed framework is validated using extensive simulation experiments. We show how data integrity scores vary under a variety of system factors like attack intensity and inaccurate detection.