Fault Tolerant Aggregation Research Articles

Fault-Tolerant Aggregate Signature Schemes against Bandwidth Consumption Attack

A fault-tolerant aggregate signature (FT-AS) scheme is a variant of an aggregate signature scheme with the additional functionality to trace signers that create invalid signatures in case an aggregate signature is invalid. Several FT-AS schemes have been proposed so far, and some of them trace such rogue signers in multi-rounds, i.e., the setting where the signers repeatedly send their individual signatures. However, it has been overlooked that there exists a potential attack on the efficiency of bandwidth consumption in a multi-round FT-AS scheme. Since one of the merits of aggregate signature schemes is the efficiency of bandwidth consumption, such an attack might be critical for multi-round FT-AS schemes. In this paper, we propose a new multi-round FT-AS scheme that is tolerant of such an attack. We implement our scheme and experimentally show that it is more efficient than the existing multi-round FT-AS scheme if rogue signers randomly create invalid signatures with low probability, which for example captures spontaneous failures of devices in IoT systems.

An AI-driven fault-tolerant aggregation model for smart grid

An AI-driven fault-tolerant aggregation model for smart grid

Heterogeneous Fault-Tolerant Aggregate Signcryption with Equality Test for Vehicular Sensor Networks

The vehicular sensor network (VSN) is an important part of intelligent transportation, which is used for real-time detection and operation control of vehicles and real-time transmission of data and information. In the environment of VSN, massive private data generated by vehicles are transmitted in open channels and used by other vehicle users, so it is crucial to maintain high transmission efficiency and high confidentiality of data. To deal with this problem, in this paper, we propose a heterogeneous fault-tolerant aggregate signcryption scheme with an equality test (HFTAS-ET). The scheme combines fault-tolerant and aggregate signcryption, which not only makes up for the deficiency of low security of aggregate signature, but also makes up for the deficiency that aggregate signcryption cannot tolerate invalid signature. The scheme supports one verification pass when all signcryptions are valid, and it supports unbounded aggregation when the total number of signcryptions grows dynamically. In addition, this scheme supports heterogeneous equality test, and realizes the access control of private data in different cryptographic environments, so as to achieve flexibility in the application of our scheme and realize the function of quick search of plaintext or ciphertext. Then, the security of HFTAS-ET is demonstrated by strict theoretical analysis. Finally, we conduct strict and standardized experimental operation and performance evaluation, which shows that the scheme has better performance.

Privacy-preserving and fault-tolerant aggregation of time-series data without TA

Privacy-preserving and fault-tolerant aggregation of time-series data without TA

Privacy-Preserving and Fault-Tolerant Aggregation of Time-Series Data With a Semi-Trusted Authority

Time-series data aggregation in Internet of Things applications is a useful operation, where the time-series data is sensed by a group of users, and gathered by the aggregator for real-time analysis. However, some security and privacy challenges still affect the collection and aggregation process. Although existing privacy-preserving solutions achieve strong privacy guarantees, they introduce a fully trusted TA that is difficult to realize in the real world. Besides, they cannot be directly applied in time-series data aggregation scenarios due to unacceptable efficiency. In this article, we propose a privacy-preserving time-series data aggregation scheme with a semi-trusted authority. Moreover, our scheme also supports arbitrary aggregate functions and fault tolerance to enhance the reliability and scalability of data aggregation. Security analysis demonstrates that our proposed scheme achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n-k)$ </tex-math></inline-formula> -source anonymity even if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k(k\leq (n-2))$ </tex-math></inline-formula> data providers collude with the cloud server. We also conduct thorough experiments based on a simulated data aggregation scenario to show the high computation and communication efficiency of our scheme.

Nested cover-free families for unbounded fault-tolerant aggregate signatures

Aggregate signatures are used to create one short proof of authenticity and integrity from a set of digital signatures. However, one invalid signature in the set invalidates the entire aggregate, giving no information on which signatures are valid. Hartung et al. (2016) [6] propose a fault-tolerant aggregate signature scheme based on combinatorial group testing. Given a bound d on the number of invalid signatures among n signatures to be aggregated, this scheme uses d-cover-free families to determine which signatures are invalid. These combinatorial structures guarantee a moderate increase on the size of the aggregate signature that can reach the best possible compression ratio of O(nlog⁡n), for fixed d, coming from an information theoretical bound. The case where the total number of signatures grows dynamically (unbounded scheme) was not satisfactorily solved in their original paper, since explicit constructions had constant compression ratios. In the present paper, we propose efficient solutions for the unbounded scheme, relying on sequences of d-cover-free families that we call nested families. Some of our constructions yield high compression ratio close to the best known upper bound. We also propose the use of (d,λ)-cover-free families to support the loss of up to λ−1 parts of the aggregate.

The evolution of the ALICE O2 monitoring system

The ALICE Experiment was designed to study the physics of strongly interacting matter with heavy-ion collisions at the CERN LHC. A major upgrade of the detector and computing model (O2, Offline-Online) is currently ongoing. The ALICE O2 farm will consist of almost 1000 nodes enabled to read out and process on-the-fly about 27 Tb/s of raw data. To efficiently operate the experiment and the O2 facility a new monitoring system was developed. It will provide a complete overview of the overall health, detect performance degradation and component failures by collecting, processing, storing and visualising data from hardware and software sensors and probes. The core of the system is based on Apache Kafka ensuring high throughput, fault-tolerant and metric aggregation, processing with the help of Kafka Streams. In addition, Telegraf provides operating system sensors, InfluxDB is used as a time-series database, Grafana as a visualisation tool. The above tool selection evolved from the initial version where collectD was used instead of Telegraf, and Apache Flume together with Apache Spark instead of Apache Kafka.

Improved Fault-Tolerant Aggregate Signatures

Fault-tolerant aggregate signatures allow the verification algorithm to recognize and verify all the valid individual signatures in an aggregate signature. However, in ordinary aggregate signature schemes, if there is a single faulty individual signature in the valid aggregate, the whole aggregate will be invalid. This will make great difficulties in many applications including secure logging and batch verification in vehicular ad hoc network et al. In this paper, inspired by the finite set theory called uniform (k,n)-set proposed by Cao, we put forward a novel fault-tolerant aggregate signature scheme. Our new scheme is more efficient compared with previous fault-tolerant aggregate signature scheme based on cover-free family. It is noted that our scheme is quite easy to be implemented in the real scenario.

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.

Fault-tolerant aggregation: Flow-Updating meets Mass-Distribution

Flow-Updating (FU) is a fault-tolerant technique that has proved to be efficient in practice for the distributed computation of aggregate functions in communication networks where individual processors do not have access to global information. Previous distributed aggregation protocols, based on repeated sharing of input values (or mass) among processors, sometimes called Mass-Distribution (MD) protocols, are not resilient to communication failures (or message loss) because such failures yield a loss of mass. In this paper, we present a protocol which we call Mass-Distribution with Flow-Updating (MDFU). We obtain MDFU by applying FU techniques to classic MD. We analyze the convergence time of MDFU showing that stochastic message loss produces low overhead. This is the first convergence proof of an FU-based algorithm. We evaluate MDFU experimentally, comparing it with previous MD and FU protocols, and verifying the behavior predicted by the analysis. Finally, given that MDFU incurs a fixed deviation proportional to the message-loss rate, we adjust the accuracy of MDFU heuristically in a new protocol called MDFU with Linear Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave very well in practice, even under high rates of message loss and even changing the input values dynamically.

AGRIB-BHF: A sustainable and fault tolerant aggregation

Growing size of the internet is raising concern to internet designers as the routing table growth leading to the state of difficulty in managing the huge amount of forwarding information on relatively limited memory size of line cards. Aggregation is a method of limiting the volume of data in the forwarding information base. Most of the existing approaches have shown aggressive aggregation tendency that leaves problems of black holes, overburden next hops links, inappropriate path selection in the forwarding information which subsequently degrades the overall performance of the internet. This paper proposes an approach AGRIB-BHF to overcome aforementioned problems of aggregation.

Fault-tolerant aggregator election and data aggregation in wireless sensor networks

This paper presents three algorithms for aggregator election and data aggregation in wireless sensor networks where sensors can crash and recover. The network is divided in several regions. The algorithms ensure the election of a common data aggregator sensor within each region, in charge of the collection of local data and the election of a unique super-aggregator sensor, in charge of the collection of global data, among all the aggregators. Both elections are achieved by implementing the Omega failure detector, which provides a self-organising and fault-tolerant leader election service. Each algorithm is based on a different connectivity assumption. The first algorithm assumes that every pair of sensors in a region can communicate directly. The second algorithm only requires some correct sensor to communicate directly with the rest of sensors. Finally, the third algorithm only requires the existence of a multi-hop bidirectional path from some correct sensor to the rest of sensors. We also introduce a battery depletion threshold to enhance the quality of service of the wireless sensor network.

Fault tolerant aggregation in heterogeneous sensor networks

Fault tolerance and scalability are important considerations in the design of sensor network applications. Data aggregation is an essential operation in sensor networks. Multiple techniques have been proposed recently to tackle the issues of scalability and fault tolerance of aggregation in sensor networks. In this article, we analyze the impact of using a few of the more reliable, though expensive, nodes–such as the Intel XScale–called microservers, in addition to the standard motes, on the fault tolerance and scalability of the aggregation algorithms in sensor networks. In particular, we propose a simple model that captures the essence of tree aggregation in such heterogeneous sensor networks. We validate this theoretical model with simulation results. We also study the effective impact on the sustainable probability of failure, and perform cost-benefit analysis. We also show how hybrid aggregation can be utilized instead of tree, to improve the performance of aggregation in heterogeneous sensor networks. We show that our work can be applied for effectively optimizing the use of expensive hardware while designing fault-tolerant, distributed sensor networks.