Privacy-preserving Data Aggregation Protocol Research Articles

Trustless privacy-preserving data aggregation on Ethereum with hypercube network topology

The privacy-preserving data aggregation is a critical problem for many applications where multiple parties need to collaborate with each other privately to arrive at certain results. Blockchain, as a database shared across the network, provides an underlying platform on which such aggregations can be carried out with a decentralized manner. Therefore, in this paper, we have proposed a scalable privacy-preserving data aggregation protocol for summation on the Ethereum blockchain by integrating several cryptographic primitives including commitment scheme, asymmetric encryption and zero-knowledge proof along with the hypercube network topology. The protocol consists of four stages as contract deployment, user registration, private submission and proof verification. The analysis of the protocol is made with respect to two main perspectives as security and scalability including computational, communicational and storage overheads. In the paper, the zero-knowledge proof, smart contract and web user interface models for the protocol are provided. We have performed an experimental study in order to identify the required gas costs per individual and per system. The general formulation is provided to characterize the changes in gas costs for the increasing number of users. The zero-knowledge proof generation and verification times are also measured.

A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

Privacy-preserving data aggregation scheme against deletion and tampering attacks from aggregators

Because privacy-preserving data aggregation protocols provide data privacy and data compression, they have been extensively studied in smart grids. However, most of the existing data aggregation protocols are based on honest-but-curious aggregators, or adopt computationally intensive asymmetric homomorphic encryption, making these protocols only provably secure in weak security models, or resulting in high computational complexity for terminal users. To address the aforementioned issues, in this work, we propose a novel lightweight privacy-preserving data aggregation scheme against malicious aggregators based on our symmetric homomorphic encryption. The proposed scheme can reduce the computational cost of smart meters while also resisting tampering and deletion attacks from malicious aggregators. One of the highlights of the proposed scheme is its ability to withstand deletion attacks from malicious aggregators, which makes it unnecessary for the malicious aggregator to aggregate the readings of all smart meters instead of only a part of them. Therefore, our scheme is more secure than other schemes. It is the first privacy-preserving data aggregation scheme based on a stronger security model without multiple rounds of interaction. Finally, the detailed security analysis shows that the proposed scheme satisfies desirable security properties. Moreover, experimental results demonstrate that the proposed scheme is superior to the other four schemes in terms of computational complexity and communication overhead.

Privacy-Preserving Outsourcing Algorithms for Multidimensional Data Encryption in Smart Grids.

With the development of the Internet of Things, smart grids have become indispensable in our daily life and can provide people with reliable electricity generation, transmission, distribution and control. Therefore, how to design a privacy-preserving data aggregation protocol has been a research hot-spot in smart grid technology. However, these proposed protocols often contain some complex cryptographic operations, which are not suitable for resource-constrained smart meter devices. In this paper, we combine data aggregation and the outsourcing of computations to design two privacy-preserving outsourcing algorithms for the modular exponentiation operations involved in the multi-dimensional data aggregation, which can allow these smart meter devices to delegate complex computation tasks to nearby servers for computing. By utilizing our proposed outsourcing algorithms, the computational overhead of resource-constrained smart meter devices can be greatly reduced in the process of data encryption and aggregation. In addition, the proposed algorithms can protect the input’s privacy of smart meter devices and ensure that the smart meter devices can verify the correctness of results from the server with a very small computational cost. From three aspects, including security, verifiability and efficiency, we give a detailed analysis about our proposed algorithms. Finally, through carrying out some experiments, we prove that our algorithms can improve the efficiency of performing the data encryption and aggregation on the smart meter device side.

Probabilistic Counters for Privacy Preserving Data Aggregation

Probabilistic counters are well known tools often used for space-efficient set cardinality estimation. In this paper we investigate probabilistic counters from the perspective of preserving privacy. We use standard, rigid differential privacy notion. The intuition is that the probabilistic counters do not reveal too much information about individuals, but provide only general information about the population. Thus they can be used safely without violating privacy of individuals. It turned out however that providing a precise, formal analysis of privacy parameters of probabilistic counters is surprisingly difficult and needs advanced techniques and a very careful approach. We demonstrate also that probabilistic counters can be used as a privacy protecion mechanism without any extra randomization. That is, the inherit randomization from the protocol is sufficient for protecting privacy, even if the probabilistic counter is used many times. In particular we present a specific privacy-preserving data aggregation protocol based on a probabilistic counter. Our results can be used for example in performing distributed surveys.

Efficient data management for urban logistics distribution

At present, the urban population accounts for an increasing proportion of the total population, and the dense urban population has also caused some problems. To solve the issue of delayed material delivery, urban logistics distribution has become one of the key solutions. With the rapid improvement of sensor nodes and wireless technique, time-series data applications become increasingly popular. In an urban logistics distribution application, the vehicles can be the sensing user while the statistics data user is the data aggregator. In many cases, the aggregator is untrusted which makes it much more difficult to protect sensing users' privacy. In this paper, we take the multi-threads smart devices into consideration, and propose a privacy-preserving protocol which largely decreases decryption computation overheads. After importing multi-threads computation, we enhance a scalable privacy-preserving data aggregation protocol to make it less time costly and more suitable for large sensing system.

Lightweight privacy-preserving data aggregation protocol against internal attacks in smart grid

Privacy-preserving data aggregation has been studied extensively over the past decades, but there are still some concerns remained. For example, some schemes cannot resist against internal attacks, especially when the internal attack is launched by either the data centers that allocate the system security parameters or the attacker who shares the common information with the targeted user. In this paper, we propose a lightweight privacy-preserving data aggregation scheme, which is more efficient and suitable for the resource-constrained devices. Our scheme aggregates total electricity consumption data in the smart grid, with the capability of resisting against the collusion attacks with (n − 1) users. In the evaluation, we investigate the performances in the aspects of computation and communication costs as compared with the state-of-the-art, and the results show that our scheme is practical for the current smart grid environment.

Aggregate in my way: Privacy-preserving data aggregation without trusted authority in ICN

Information-Centric Networking (ICN) is a novel future network architecture which in contrast to IP-based networks relies on content and its name. It separates the physical location of data from the discovery and forwarding process and solely relies on the content itself. For the Internet of Thing (IoT) networks, stripping the location information may provide privacy, but this does not translate to operational privacy. Attackers can infer user behavior patterns through operational privacy, by eavesdropping on meaningful information. The content name and designed signature in ICN can associate content with the identity of the provider, even if the IP address is hidden. Although several research efforts focus on the privacy protection of ICN, they do not consider the privacy implications of data aggregation. Most existing privacy-preserving data aggregation protocols are designed for special operations, such as sum, average, variance, max or min, and they cannot support arbitrary aggregation operations in the ciphertexts domain. Besides, the need for trusted authority (TA) restricts the use of existing protocols in the real world. In this paper, we propose a practical and privacy-preserving data aggregation scheme that can compute arbitrary aggregation functions without a TA. On one hand, our scheme can ensure users’ anonymity and privacy protection, while on the other, the scheme is efficient in enabling participants to join or leave the system dynamically. Security analysis shows that the proposed scheme can achieve the desired security properties, while experimental results demonstrate its effectiveness and efficiency.

Fog computing‐based privacy preserving data aggregation protocols

AbstractAs the number of active Internet of Things (IoT) devices increases, managing and securing the enormous amount of data generated by these devices become harder. With their always‐connected smart meters and internet‐enabled electrical appliances, smart grids (SGs) are major data generation sources in IoT. Traditional SGs store and process their collected data in centralized cloud servers. Secure data aggregation (SDA) protocols play an essential role in efficient and private data collection and storage in cloud‐based SGs. Due to a high number of connected devices, centralized cloud‐based SGs may become inadequate to satisfy the processing and storage needs of the collected data. This article introduces two efficient and lightweight SDA protocols for fog computing‐based SGs (FCSG), namely, FCSG‐DF and FCSG‐P, as a complementary solution for processing and storage needs of cloud‐based SGs. By leveraging the distributed nature and other extended capabilities of fog computing, the proposed SDA protocols have a better response time with less computational overhead. Moreover, the privacy of fine‐grained energy consumption data is preserved with the help of the privacy‐preserving data aggregation processes in the hierarchical fog computing structure of FCSG. Extensive performance evaluation results demonstrate that the proposed protocols have superiority in terms of data transmission and storage efficiency compared to cloud‐assisted schemes without data aggregation. Both protocols are analyzed by comparing their success in transmission and storage efficiencies. Moreover, privacy analysis verifies that both protocols effectively ensure the privacy of the data in each layer.

Energy-efficient privacy-preserving data aggregation protocols based on slicing

Wireless sensor networks (WSNs) have become one of the most vigorous techniques in the network domain. However, the sensor nodes of WSNs tend to become the target of attackers due to the broadcast communication mode and the unattended deployment nature. Although it can prevent the sensitive data from being compromised, Slice-Mix-AggRegaTe (SMART) needs to exchange messages frequently in a network, which put tremendous overhead on the sensor nodes with limited resources. Faced with these issues, this paper proposes an energy-efficient privacy-preserving data aggregation protocol based on slicing (EPPA) where a novel slicing mode is adopted to reduce the numbers of slices, which can significantly prevent the data from being compromised and decrease the communication overhead. Meanwhile, an enhanced scheme based on EPPA, called multi-function privacy-preserving data aggregation protocol (MPPA), is presented and it supports multiple functions in the process of data aggregation, such as max/min, count, and mean. The theoretical analysis and the simulation evaluation show that the proposed aggregation protocols demonstrate a better performance in the privacy preserving and the communication efficiency.

NLDA non-linear regression model for preserving data privacy in wireless sensor networks

Recently, the application of Wireless Sensor Networks (WSNs) has been increasing rapidly. It requires privacy preserving data aggregation protocols to secure the data from compromises. Preserving privacy of the sensor data is a challenging task. This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data. The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes. Instead of sending the complete data to the cluster head, the sensor nodes only send the coefficients of the non-linear function. This will reduce the communication overhead of the network. The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data. The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead, enhance data aggregation accuracy, and preserve data privacy.

On practical privacy-preserving fault-tolerant data aggregation

In this paper, we propose a fault-tolerant privacy-preserving data aggregation protocol which utilizes limited local communication between nodes. As a starting point, we analyze the Binary Protocol presented by Chan et al. Comparing to previous work, their scheme guaranteed provable privacy of individuals and could work even if some number of users refused to participate. In our paper we demonstrate that despite its merits, their method provides unacceptably low accuracy of aggregated data for a wide range of assumed parameters and cannot be used in majority of real-life systems. To show this we use both analytic and experimental methods. On the positive side, we present a precise data aggregation protocol that provides provable level of privacy even when facing massive failures of nodes. Moreover, our protocol requires significantly less computation (limited exploiting of heavy cryptography) than most of currently known fault-tolerant aggregation protocols and offers better security guarantees that make it suitable for systems of limited resources (including sensor networks). Most importantly, our protocol significantly decreases the error (compared to Binary Protocol). However, to obtain our result we relax the model and allow some limited communication between the nodes. Our approach is a general way to enhance privacy of nodes in networks that allow such limited communication, i.e., social networks, VANETs or other IoT appliances. Additionally, we conduct experiments on real data (Facebook social network) to compare our protocol with protocol presented by Chan et al.

Fast computation method for privacy-preserving data aggregation protocol

Fast computation method for privacy-preserving data aggregation protocol

Fast computation method for privacy-preserving data aggregation protocol

Recent years have seen the rapid improvement of smart terminals and wireless networks, and a lot of smart applications have come to the fore. Taking advantages of fast developed smart terminals, more and more complicated sensing tasks can be taken over and finished. Data aggregation is an important application which is required by many tasks. The sensing devices in mobile sensing systems get time-series data, which can be used for supply forecast and leakage detection. As people nowadays pay more attention on privacy issues, to let people use today's data aggregation application, the first task is to solve their privacy concerns. Here, we propose a fast encryption/decryption mechanism for addition operations on keyed hash functions, based on multi-threads computation and parallel computation on multi-cores architecture. The advanced protocol presented by us works better for large systems and requires less time cost for both mobile sensors and aggregator. The experiments show the performance benefits of the presented protocol.

Energy-saving privacy-preserving data aggregation protocol with mobile sink

Wireless sensor network uses a large number of sensing nodes to collect real-time data of the deployed environment, and then send these data to the base station. For limited resources and energy, we have to consider the energy consumption problem in practical applications. In addition, data aggregation protocols need privacy protection. We have found out the high energy cost issue of the basic privacy-preserving data aggregation protocol and introduce two novel elements into the basic protocol: mobile sink and straw-man structure. Combining these two novel elements, we propose the new energy-saving privacy-preserving data aggregation protocol. In the basic protocol, the sensor nodes in each sensing period need to receive and send multiple ciphertexts. While in our proposed new protocol, the sensor nodes in each sensing period only need to send a ciphertext. Therefore, our protocol can reduce the energy consumption of the whole network and prolong the service life of wireless sensor networks.

Energy-saving privacy-preserving data aggregation protocol with mobile sink

Energy-saving privacy-preserving data aggregation protocol with mobile sink

Privacy-preserving data aggregation in two-tiered wireless sensor networks with mobile nodes.

Privacy-preserving data aggregation in wireless sensor networks (WSNs) with mobile nodes is a challenging problem, as an accurate aggregation result should be derived in a privacy-preserving manner, under the condition that nodes are mobile and have no pre-specified keys for cryptographic operations. In this paper, we focus on the SUM aggregation function and propose two privacy-preserving data aggregation protocols for two-tiered sensor networks with mobile nodes: Privacy-preserving Data Aggregation against non-colluded Aggregator and Sink (PDAAS) and Privacy-preserving Data Aggregation against Colluded Aggregator and Sink (PDACAS). Both protocols guarantee that the sink can derive the SUM of all raw sensor data but each sensor's raw data is kept confidential. In PDAAS, two keyed values are used, one shared with the sink and the other shared with the aggregator. PDAAS can protect the privacy of sensed data against external eavesdroppers, compromised sensor nodes, the aggregator or the sink, but fails if the aggregator and the sink collude. In PDACAS, multiple keyed values are used in data perturbation, which are not shared with the aggregator or the sink. PDACAS can protect the privacy of sensor nodes even the aggregator and the sink collude, at the cost of a little more overhead than PDAAS. Thorough analysis and experiments are conducted, which confirm the efficacy and efficiency of both schemes.

An Enforcing Integrity and Privacy-Preserving Data Aggregation Protocol in Wireless Sensor Network

It is important to ensure the integrity of the aggregation result and quality of communication among node in wireless sensor network in the condition of high-efficient privacy-preserving data aggregation mechanism. In this paper, we propose a method based on reputation model to solve this problem. During the process of data aggregation, the cluster head node will choose the node with max reputation value as collaborative node to accomplish the aggregation process. To show the efficacy and efficiency of this method, we present the simulation results. The simulation results show that our method iE-CPDA(enforced Integrity Energy-efficient Cluster-based Privacy-preserving Data Aggregation) has less communication and computation and higher accuracy than iCPDA.

Integrity Protecting and Privacy Preserving Data Aggregation Protocols in Wireless Sensor Networks: A Survey

The data aggregation is a widely used energy- efficient mechanism in wireless sensor Networks (WSNs), by avoiding the redundant data transmitting to base station. The deployment of wireless communicating sensor nodes in the hostile or unattended environment causes attack more easily and the resource limited characteristics make the conventional security algorithms infeasible, hence protecting privacy and integrity during data aggregation is a challenging task. The privacy of a sensor data ensures, it is known only to itself and the integrity guarantees sensor data has not tampered during data aggregation. The Integrity Protecting Privacy preserving Data Aggregation (IPPDA) protocols ensures a robust and accurate results at the base station. This paper summarises on such IPPDA protocols during data aggregation.

Preserving privacy against external and internal threats in WSN data aggregation

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.