BES: Differentially private event aggregation for large-scale IoT-based systems

Tudor, Valentin; Gulisano, Vincenzo; Almgren, Magnus; Papatriantafilou, Marina

doi:10.1016/j.future.2018.07.026

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…The spectrum of possible solutions for hardly separable sets includes, e.g., the method to add the calibrated noise component to data; this concept was discussed in the paper [35], which was devoted to the problem of events aggregation in large-scale IoT systems. However, the guaranteed mathematically correct method of arbitrary data separation is proposed in Section 3 to be based on MVL function.…”

Section: Actual Methods Of Data Clusteringmentioning

confidence: 99%

“…, if and only if both conditions are true: As in the notation 1 * (35,59) , the first product term is included into the second one and can be deleted, thus it was crossed out.…”

Section: The Minimization Methods For Aga Functionsmentioning

confidence: 99%

“…Within the framework of the given paper, the actual AI methods [27] can be mainly subdivided into multiagent systems (MAS), neural networks, and fuzzy logic systems. MAS are primarily applied for the modeling of software and hardware agents in the tasks of interaction with the environment and other agents, as well as for space positioning [28][29][30][31][32][33][34][35]. Modern versions of deep learning neural networks are concentrated on computer vision and speech procession [36][37][38][39].…”

Section: Actual Problems For Communication Networkmentioning

confidence: 99%

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Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

Bykovsky

2020

Quantum Reports

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Quantum optics is regarded as the acknowledged method to provide network quantum keys distribution and in the future secure distributed quantum computing, but it should also provide cryptography protection for mobile robots and the Internet of Things (IoT). This task requires the design of new secret coding schemes, which can be also based on multiple-valued logic (MVL). However, this very specific logic model reveals new possibilities for the hierarchical data clustering of arbitrary data sets. The minimization of multiple-valued logic functions is proposed for the analysis of aggregated objects, which is possible for an arbitrary number of variables. In order to use all the useful properties of the multiple-valued logic, the heterogeneous network architecture is proposed, which includes three allocated levels of artificial intelligence (AI) logic modeling for discrete multiple-valued logic, Boolean logic, and fuzzy logic. Multiple-valued logic is regarded as the possible platform for additional secret coding, data aggregation, and communications, which are provided by the united high dimensional space for network addressing and the targeted control of robotic devices. Models of Boolean and fuzzy logic are regarded as separate logic levels in order to simplify the integration of various algorithms and provide control of additional data protection means for robotic agents.

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Section: Actual Methods Of Data Clusteringmentioning

confidence: 99%

“…, if and only if both conditions are true: As in the notation 1 * (35,59) , the first product term is included into the second one and can be deleted, thus it was crossed out.…”

Section: The Minimization Methods For Aga Functionsmentioning

confidence: 99%

Section: Actual Problems For Communication Networkmentioning

confidence: 99%

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Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

Bykovsky

2020

Quantum Reports

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“…They present a theoretical framework and empirically investigate how changing the properties of the available data would change an attacker's success. Also, differential privacy has been suggested to protect customers [67], where Tudor et al design and evaluate a prototype based on a streaming framework to scale to very large data. By adding noise, there is a risk that the usefulness of the data collected will decrease.…”

Section: Related Workmentioning

confidence: 99%

“…However, one disadvantage with the above approaches is that for billing, privacy will cost. Changing the granularity of the collected data as in [66] or using differential privacy as in [67], mean that a customer is not charged for her actual consumption. It should be noted that limiting the consequences to the utility of noise-adding techniques is an ongoing research challenge (see, e.g., [35]).…”

Section: Related Workmentioning

confidence: 99%

Gridchain: an investigation of privacy for the future local distribution grid

Picazo-Sanchez

Almgren

2022

Int. J. Inf. Secur.

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As part of building the smart grid, there is a massive deployment of so-called smart meters that aggregate information and communicate with the back-end office, apart from measuring properties of the local network. Detailed measurements and communication of, e.g., consumption allows for remote billing, but also in finding problems in the distribution of power and overall to provide data to be used to plan future upgrades of the network. From a security perspective, a massive deployment of such Internet of Things (IoT) components increases the risk that some may be compromised or that collected data are used for privacy-sensitive inference of the consumption of households. In this paper, we investigate the privacy concerns regarding detailed readings of smart meters for billing purposes. We present Gridchain, a solution where households can opt-in to hide their consumption patterns and thus make Non-Intrusive Load Monitoring (NILM) more challenging. Households form groups where they can trade real consumption among themselves to achieve reported consumption that would be resistant to NILM. Gridchain is built on a publish/subscribe model and uses a permissioned blockchain to record any trades, meaning that dishonest households can be discovered and punished if they steal from other households in the group or the electricity company in the end. We implement and release a proof of concept of Gridchain and use public datasets to allow reproducibility. Our results show that even if an attacker has access to the reported electricity consumption of any member of a Gridchain group, this reported consumption is significantly far from the actual consumption to allow for a detailed fingerprint of the household activities.

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Internet of Things‐based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

Gunasekaran

2023

Int J Communication

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SummaryA wireless sensor network (WSN) is a network of autonomous, small sensors that can detect, collect, and send data about their surrounding environment. In the Internet of Things (IoT) infrastructure, WSNs are the smart devices that provide the platform with resource input. Security breaches and insider attacks are possible due to the WSN's resource‐constrained design. However, the IoT platform's intelligence may be extended to WSN nodes for managing device and data‐level security. This paper proposes Monitored Access Constraint Security (MACS) to ensure the privacy of data collected via the ubiquitous processing enabled by the Internet of Things. The IoT platform performs frequent checks on the quality of the interactions between the various nodes to ensure that they are functioning properly and that the sensor aggregation instances are accountable. Node liability is considered while adjusting the aggregate level and the continuity. The method guarantees secure information from the environment and the data sources. The quality of the data gathered in the suggested technique is evaluated based on node liability and information extraction feature. Accordingly, security measures are implemented at data gathering and filtering levels and then assessed using a recurrent learning process. Since there are fewer security breaches overall, the rate of aggregation increases. Aggregation loss, delay time, false rate, throughput, and verification time are used to evaluate the performance.

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BES: Differentially private event aggregation for large-scale IoT-based systems

Cited by 11 publications

References 14 publications

Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

Gridchain: an investigation of privacy for the future local distribution grid

Internet of Things‐based wireless sensor networks for monitoring access constraint security measures in liable data aggregation

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