The Wireless Sensor Networks (WSNs) are composed of resource starved sensor nodes that are deployed to sense, process and communicate vital information to the base station. Due to the stringent constraints on the resources in the sensor nodes on one hand and due to the communications costs being always significantly higher than the data processing costs, the WSNs typically, employ in-network processing, which aims at reducing effectively, the total number of packets eventually transmitted to the base station. Such innetwork processing largely employs data aggregation operations that aggregate the data into a compact representation for further transmission. However, due to the ubiquitous & pervasive deployment, heavier resource demands of the security protocols and due to the stringent resource constraints in WSN nodes, the security concerns in WSNs are even otherwise critical. These concerns assume alarming proportions when using data aggregation in which the output of the data aggregator nodes depends on that of various other nodes. Hence, the protocols for data aggregation have to carefully devised with a constant vigil on ensuring security of the data. In this paper, based on our survey of the existing research efforts for ensuring secure data aggregation, we propose a novel approach using homomorphic encryption and additive digital signatures to achieve confidentiality, integrity and availability for secure data aggregation in wireless sensor networks.
Much research has been conducted to securely outsource multiple parties' data aggregation to an untrusted aggregator without disclosing each individual's data, or to enable multiple parties to jointly aggregate their data while preserving privacy. However, those works either assume to have a secure channel or suffer from high complexity. Here we consider how an external aggregator or multiple parties learn some algebraic statistics (e.g., summation, product) over participants' data while any individual's input data is kept secret to others (the aggregator and other participants). We assume cha…
Two recent papers describe almost exactly the same group key establishment protocol for wireless sensor networks. Quite part from the duplication issue, we show that both protocols are insecure and should not be used - a member of a group can successfully impersonate the key generation centre and persuade any other group member to accept the wrong key value. This breaks the stated objectives of the schemes.
Secure Multiparty Computation (MPC) can improve the security and privacy of data owners while allowing analysts to perform high quality analytics. Secure aggregation is a secure distributed mechanism to support federated deep learning without the need for trusted third parties. In this paper we present a highly performant secure aggregation protocol with sub-linear communication complexity. Our protocol achieves greater communication and computation efficiencies through a group-based approach. It is similar to secret sharing protocols extended to vectors of values-aka gradients-but within grou…
Recently, secure in-network aggregation in wireless sensor networks becomes a challenge issue, there is an extensive research on this area due to the large number of applications where the sensors are deployed and the security needs. In the last few years, aggregation of encrypted data has been proposed in order to maintain secrecy between the sensors and the sink, so the end-to-end data confidentiality is provided. However, the data integrity was not addressed. In this paper, we propose RSAED that allows integrity verification at intermediate nodes, ensures the base station to receive ciphert…
Secure aggregation is widely used in horizontal Federated Learning (FL), to prevent leakage of training data when model updates from data owners are aggregated. Secure aggregation protocols based on Homomorphic Encryption (HE) have been utilized in industrial cross-silo FL systems, one of the settings involved with privacy-sensitive organizations such as financial or medical, presenting more stringent requirements on privacy security. However, existing HE-based solutions have limitations in efficiency and security guarantees against colluding adversaries without a Trust Third Party. This paper…
Secure linear aggregation is to linearly aggregate private inputs of different users with privacy protection. The server in a federated learning (FL) environment can fulfill any linear computation on private inputs of users through the secure linear aggregation. At present, based on pseudo-random number generator and one-time padding technique, one can efficiently compute the sum of user inputs in FL, but linear calculations of user inputs are not well supported. Based on decentralized threshold additive homomorphic encryption (DTAHE) schemes, this paper provides a secure linear aggregation pr…
Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of secure aggregation prevents the server from learning the value and the source of the individual model updates provided by the users, hampering inference and data attribution attacks. In this work, we show that a malicious server can easily elude secure aggregation as if the latter were not in place. We devise two different attacks capable of inferring information on individual private training datasets, ind…
Previous Web access authentication systems often use either the Web or the Mobile channel individually to confirm the claimed identity of the remote user. This paper proposes a new protocol using multifactor authentication system that is both secure and highly usable. It uses a novel approach based on Transaction Identification Code and SMS to enforce extra security level with the traditional Login/password system. The system provides a highly secure environment that is simple to use and deploy, that does not require any change in infrastructure or protocol of wireless networks. This Protocol …
Privacy preserving multi-party computation has many applications in areas such as medicine and online advertisements. In this work, we propose a framework for distributed, secure machine learning among untrusted individuals. The framework consists of two parts: a two-step training protocol based on homomorphic addition and a zero knowledge proof for data validity. By combining these two techniques, our framework provides privacy of per-user data, prevents against a malicious user contributing corrupted data to the shared pool, enables each user to self-compute the results of the algorithm with…
We propose and experimentally evaluate a novel secure aggregation algorithm targeted at cross-organizational federated learning applications with a fixed set of participating learners. Our solution organizes learners in a chain and encrypts all traffic to reduce the controller of the aggregation to a mere message broker. We show that our algorithm scales better and is less resource demanding than existing solutions, while being easy to implement on constrained platforms. With 36 nodes our method outperforms state-of-the-art secure aggregation by 70x, and 56x with and without failover, respecti…
This work introduces FAIR, a novel framework for Fuzzy-based Aggregation providing In-network Resilience for Wireless Sensor Networks. FAIR addresses the possibility of malicious aggregator nodes manipulating data. It provides data-integrity based on a trust level of the WSN response and it tolerates link or node failures. Compared to available solutions, it offers a general aggregation model and makes the trust level visible to the querier. We classify the proposed approach as complementary to protocols ensuring resilience against sensor leaf nodes providing faulty data. Thanks to our flexibl…
Although homomorphic encryption can be incorporated into neural network layers for securing machine learning tasks, such as confidential inference over encrypted data samples and encrypted local models in federated learning, the computational overhead has been an Achilles heel. This paper proposes a caching protocol, namely CHEM, such that tensor ciphertexts can be constructed from a pool of cached radixes rather than carrying out expensive encryption operations. From a theoretical perspective, we demonstrate that CHEM is semantically secure and can be parameterized with straightforward analys…
In [3], the authors proposed a highly efficient secure and privacy-preserving scheme for secure vehicular communications. The proposed scheme consists of four protocols: system setup, protocol for STP and STK distribution, protocol for common string synchronization, and protocol for vehicular communications. Here we define the security models for the protocol for STP and STK distribution, and the protocol for vehicular communications,respectively. We then prove that these two protocols are secure in our models.
Recently, the number of requests for multicast services through the wireless networks has been increased. However, for successful deployment, security and efficiency of content delivery must be provided at first. This paper presents a new approach for secure multicast in wireless networks. This approach, CRAW (Combination of Re-keying and Authentication in Wireless networks) combines member authentication procedure with group key management protocol to provide an efficient group re-keying process. One-time password is proposed for member authentication and CKC (Code for Key Calculation) is sug…
This chapter discusses the need of security and privacy protection mechanisms in aggregation protocols used in wireless sensor networks (WSN). It presents a comprehensive state of the art discussion on the various privacy protection mechanisms used in WSNs and particularly focuses on the CPDA protocols proposed by He et al. (INFOCOM 2007). It identifies a security vulnerability in the CPDA protocol and proposes a mechanism to plug that vulnerability. To demonstrate the need of security in aggregation process, the chapter further presents various threats in WSN aggregation mechanisms. A large n…
We consider the generic problem of Secure Aggregation of Distributed Information (SADI), where several agents acting as a team have information distributed among them, modeled by means of a publicly known deck of cards distributed among the agents, so that each of them knows only her cards. The agents have to exchange and aggregate the information about how the cards are distributed among them by means of public announcements over insecure communication channels, intercepted by an adversary "eavesdropper", in such a way that the adversary does not learn who holds any of the cards. We present a…
In Wireless Sensor Networks, the sensor nodes are battery powered small devices designed for long battery life. These devices also lack in terms of processing capability and memory. In order to provide high confidentiality to these resource constrained network nodes, a suitable security algorithm is needed to be deployed that can establish a balance between security level and processing overhead. The objective of this research work is to perform a security analysis and performance evaluation of recently proposed Secure Force algorithm. This paper shows the comparison of Secure Force 64, 128, a…
This work presents an application of the highly expressive Attribute-Based Encryption to implement wireless-delimited Secure Zones for firearms. Within these zones, radio-transmitted local policies based on attributes of the consumer and the firearm are received by embedded hardware in the firearms, which then advises the consumer about safe operations. The Secure Zones utilize Attribute-Based Encryption to encode the policies and consumer or user attributes, and providing privacy and security through it cryptography. We describe a holistic approach to evolving the firearm to a cyber-physical …
Privacy-Preserving Data Aggregation Techniques for Enhanced Efficiency and Security in Wireless Sensor Networks: A Comprehensive Analysis and Evaluation
Wireless Sensor Networks (WSNs) rely on in-network aggregation for efficiency, however, this comes at a price: A single adversary can severely influence the outcome by contributing an arbitrary partial aggregate value. Secure in-network aggregation can detect such manipulation. But as long as such faults persist, no aggregation result can be obtained. In contrast, the collection of individual sensor node values is robust and solves the problem of availability, yet in an inefficient way. Our work seeks to bridge this gap in secure data collection: We propose a system that enhances availability …
Key establishment in sensor networks becomes a challenging problem because of the resource limitations of the sensors and also due to vulnerability to physical capture of the sensor nodes. In this paper, we propose an unconditionally secure probabilistic group-based key pre-distribution scheme for a heterogeneous wireless sensor network. The proposed scheme always guarantees that no matter how many sensor nodes are compromised, the non-compromised nodes can still communicate with 100% secrecy, i.e., the proposed scheme is always unconditionally secure against node capture attacks. Moreover, it…
A secure key distribution protocol protected by light's noise was introduced in 2003 [Phys. Rev. A 68, 052307 (2003)]. That protocol utilized the shot noise of light present in the optical channel (eg., an optical fiber) to restrict information leaks to an adversary. An initial shared information between the legitimate users allowed them to extract more information from the channel than the one obtained by the adversary. That original paper recognized the need for a privacy amplification step but no specific protocol was presented. More recently that original idea was improved with a specific …
In Wireless sensor networks (WSNs), All communications between different nodes are sent out in a broadcast fashion. These networks are used in a variety of applications including military, environmental, and smart spaces. Sensors are susceptible to various types of attack, such as data modification, data insertion and deletion, or even physical capture and sensor replacement. Hence security becomes important issue in WSNs. However given the fact that sensors are resources constrained, hence the traditional intensive security algorithms are not well suited for WSNs. This makes traditional secur…
Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descent across user-held training data on mobile devices, wherein Secure Aggregation protects each user's model gradient. We design a novel, communication-efficient Secure Aggregation protocol for high-dimensional data that tolerates up to 1/3 users failing to complete the protocol. Fo…
Federated learning is a collaborative method that aims to preserve data privacy while creating AI models. Current approaches to federated learning tend to rely heavily on secure aggregation protocols to preserve data privacy. However, to some degree, such protocols assume that the entity orchestrating the federated learning process (i.e., the server) is not fully malicious or dishonest. We investigate vulnerabilities to secure aggregation that could arise if the server is fully malicious and attempts to obtain access to private, potentially sensitive data. Furthermore, we provide a method to f…
Recently, with the support of mobile cloud computing, a large number of health related data collected from various body sensor networks can be managed efficiently. However, to ensure data security and data privacy in cloud-integrated body sensor networks is an important and challenging issue. In this paper, we present a novel secure access control mechanism Mask Certificate Attribute Based Encryption for cloud integrated body sensor networks. A specific signature is designed to mask the plaintext, then the masked data can be securely outsourced to cloud severs. An authorization certificate com…
We study a general scenario where confidential information is distributed among a group of agents who wish to share it in such a way that the data becomes common knowledge among them but an eavesdropper intercepting their communications would be unable to obtain any of said data. The information is modelled as a deck of cards dealt among the agents, so that after the information is exchanged, all of the communicating agents must know the entire deal, but the eavesdropper must remain ignorant about who holds each card. Valentin Goranko and the author previously set up this scenario as the secur…
Secure aggregation is a popular protocol in privacy-preserving federated learning, which allows model aggregation without revealing the individual models in the clear. On the other hand, conventional secure aggregation protocols incur a significant communication overhead, which can become a major bottleneck in real-world bandwidth-limited applications. Towards addressing this challenge, in this work we propose a lightweight gradient sparsification framework for secure aggregation, in which the server learns the aggregate of the sparsified local model updates from a large number of users, but w…
Secure aggregation, which is a core component of federated learning, aggregates locally trained models from distributed users at a central server. The ``secure'' nature of such aggregation consists of the fact that no information about the local users' data must be leaked to the server except the aggregated local models. In order to guarantee security, some keys may be shared among the users. After the key sharing phase, each user masks its trained model which is then sent to the server. This paper follows the information theoretic secure aggregation problem originally formulated by Zhao and S…
We propose a new platform for implementing secure wireless ad hoc networks. Our proposal is based on a modular architecture, with the software stack constructed directly on the Ethernet layer. Within our platform we use a new security protocol that we designed to ensure mutual authentication between nodes and a secure key exchange. The correctness of the proposed security protocol is ensured by Guttman's authentication tests.
University of Vermont（佛蒙特大学）；MassMutual（万通互惠）；Raytheon（雷神公司）
Federated machine learning leverages edge computing to develop models from network user data, but privacy in federated learning remains a major challenge. Techniques using differential privacy have been proposed to address this, but bring their own challenges -- many require a trusted third party or else add too much noise to produce useful models. Recent advances in \emph{secure aggregation} using multiparty computation eliminate the need for a third party, but are computationally expensive especially at scale. We present a new federated learning protocol that leverages a novel differentially…
Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets. In real-world settings, this approach is hindered by expensive communication and privacy concerns. Both of these challenges have already been addressed individually, resulting in competing optimisations. In this article, we tackle them simultaneously for one of the first times. More precisely, we adapt compression-based federated techniques to additive secret sharing, leading to an efficient secure aggregation protocol, with an adaptable security level. We prove its p…
Federated learning enables multiple data owners to jointly train a machine learning model without revealing their private datasets. However, a malicious aggregation server might use the model parameters to derive sensitive information about the training dataset used. To address such leakage, differential privacy and cryptographic techniques have been investigated in prior work, but these often result in large communication overheads or impact model performance. To mitigate this centralization of power, we propose SCOTCH, a decentralized m-party secure-computation framework for federated aggreg…
The pervasive adoption of Internet-connected digital services has led to a growing concern in the personal data privacy of their customers. On the other hand, machine learning (ML) techniques have been widely adopted by digital service providers to improve operational productivity and customer satisfaction. ML inevitably accesses and processes users' personal data, which could potentially breach the relevant privacy protection regulations if not performed carefully. The situation is exacerbated by the cloud-based implementation of digital services when user data are captured and stored in dist…
Google（谷歌）；California Institute of Technology（加州理工学院）
Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud. Existing work on federated learning with limited communication demonstrates how random rotation can enable users' model updates to be quantized much more efficiently, reducing the communication cost between users and the server. Meanwhile, secure aggregation enables the server to learn an aggregate of at least a threshold number of device's model contributio…
We consider the problem of computing an aggregation function in a \emph{secure} and \emph{scalable} way. Whereas previous distributed solutions with similar security guarantees have a communication cost of $O(n^3)$, we present a distributed protocol that requires only a communication complexity of $O(n\log^3 n)$, which we prove is near-optimal. Our protocol ensures perfect security against a computationally-bounded adversary, tolerates $(1/2-ε)n$ malicious nodes for any constant $1/2 > ε> 0$ (not depending on $n$), and outputs the exact value of the aggregated function with high probability.
Aggregation of values that need to be kept confidential while guaranteeing the robustness of the process and the correctness of the result is required in an increasing number of applications. We propose an aggregation algorithm, which supports a large spectrum of potential applications including complex voting protocols. It relies on the distributed hash table Kademlia, used in BitTorrent, for pseudonymous communication between randomly predetermined peers to ensure a high degree of confidentiality which does not solely relies on cryptography. The distribution of data and computation limits th…
Wireless sensor networks (WSNs) have gained researchers' attention in the last several years. Small sensors powered by miniaturized microprocessors are capable of supporting several applications for civil and military domains. Determining the location of sensors is a basic and essential knowledge for most WSN algorithms and protocols including data tagging, routing, node identification, among others. This paper surveys the different algorithms that have been proposed to securely determine the location of a sensor node. By "secure", we mean that adversaries cannot easily affect the accuracy of …
