PAWN: a payload-based mutual authentication scheme for wireless sensor networks

Copyright © 2016 John Wiley & Sons, Ltd. Wireless sensor networks (WSNs) consist of resource-starving miniature sensor nodes deployed in a remote and hostile environment. These networks operate on small batteries for days, months, and even years depending on the requirements of monitored applications. The battery-powered operation and inaccessible human terrains make it practically infeasible to recharge the nodes unless some energy-scavenging techniques are used. These networks experience threats at various layers and, as such, are vulnerable to a wide range of attacks. The resource-constrained nature of sensor nodes, inaccessible human terrains, and error-prone communication links make it obligatory to design lightweight but robust and secured schemes for these networks. In view of these limitations, we aim to design an extremely lightweight payload-based mutual authentication scheme for a cluster-based hierarchical WSN. The proposed scheme, also known as payload-based mutual authentication for WSNs, operates in 2 steps. First, an optimal percentage of cluster heads is elected, authenticated, and allowed to communicate with neighboring nodes. Second, each cluster head, in a role of server, authenticates the nearby nodes for cluster formation. We validate our proposed scheme using various simulation metrics that outperform the existing schemes

Search Me If You Can: Privacy-preserving Location Query Service

Location-Based Service (LBS) becomes increasingly popular with the dramatic growth of smartphones and social network services (SNS), and its context-rich functionalities attract considerable users. Many LBS providers use users' location information to offer them convenience and useful functions. However, the LBS could greatly breach personal privacy because location itself contains much information. Hence, preserving location privacy while achieving utility from it is still an challenging question now. This paper tackles this non-trivial challenge by designing a suite of novel fine-grained Privacy-preserving Location Query Protocol (PLQP). Our protocol allows different levels of location query on encrypted location information for different users, and it is efficient enough to be applied in mobile platforms.Comment: 9 pages, 1 figure, 2 tables, IEEE INFOCOM 201

The Meeting of Acquaintances: A Cost-efficient Authentication Scheme for Light-weight Objects with Transient Trust Level and Plurality Approach

Wireless sensor networks consist of a large number of distributed sensor nodes so that potential risks are becoming more and more unpredictable. The new entrants pose the potential risks when they move into the secure zone. To build a door wall that provides safe and secured for the system, many recent research works applied the initial authentication process. However, the majority of the previous articles only focused on the Central Authority (CA) since this leads to an increase in the computation cost and energy consumption for the specific cases on the Internet of Things (IoT). Hence, in this article, we will lessen the importance of these third parties through proposing an enhanced authentication mechanism that includes key management and evaluation based on the past interactions to assist the objects joining a secured area without any nearby CA. We refer to a mobility dataset from CRAWDAD collected at the University Politehnica of Bucharest and rebuild into a new random dataset larger than the old one. The new one is an input for a simulated authenticating algorithm to observe the communication cost and resource usage of devices. Our proposal helps the authenticating flexible, being strict with unknown devices into the secured zone. The threshold of maximum friends can modify based on the optimization of the symmetric-key algorithm to diminish communication costs (our experimental results compare to previous schemes less than 2000 bits) and raise flexibility in resource-constrained environments.Comment: 27 page

Conditionals in Homomorphic Encryption and Machine Learning Applications

Homomorphic encryption aims at allowing computations on encrypted data without decryption other than that of the final result. This could provide an elegant solution to the issue of privacy preservation in data-based applications, such as those using machine learning, but several open issues hamper this plan. In this work we assess the possibility for homomorphic encryption to fully implement its program without relying on other techniques, such as multiparty computation (SMPC), which may be impossible in many use cases (for instance due to the high level of communication required). We proceed in two steps: i) on the basis of the structured program theorem (Bohm-Jacopini theorem) we identify the relevant minimal set of operations homomorphic encryption must be able to perform to implement any algorithm; and ii) we analyse the possibility to solve -- and propose an implementation for -- the most fundamentally relevant issue as it emerges from our analysis, that is, the implementation of conditionals (requiring comparison and selection/jump operations). We show how this issue clashes with the fundamental requirements of homomorphic encryption and could represent a drawback for its use as a complete solution for privacy preservation in data-based applications, in particular machine learning ones. Our approach for comparisons is novel and entirely embedded in homomorphic encryption, while previous studies relied on other techniques, such as SMPC, demanding high level of communication among parties, and decryption of intermediate results from data-owners. Our protocol is also provably safe (sharing the same safety as the homomorphic encryption schemes), differently from other techniques such as Order-Preserving/Revealing-Encryption (OPE/ORE).Comment: 14 pages, 1 figure, corrected typos, added introductory pedagogical section on polynomial approximatio

Privacy preserving record linkage in the presence of missing values

© 2017 The problem of record linkage is to identify records from two datasets, which refer to the same entities (e.g. patients). A particular issue of record linkage is the presence of missing values in records, which has not been fully addressed. Another issue is how privacy and confidentiality can be preserved in the process of record linkage. In this paper, we propose an approach for privacy preserving record linkage in the presence of missing values. For any missing value in a record, our approach imputes the similarity measure between the missing value and the value of the corresponding field in any of the possible matching records from another dataset. We use the k-NNs (k Nearest Neighbours in the same dataset) of the record with the missing value and their distances to the record for similarity imputation. For privacy preservation, our approach uses the Bloom filter protocol in the settings of both standard privacy preserving record linkage without missing values and privacy preserving record linkage with missing values. We have conducted an experimental evaluation using three pairs of synthetic datasets with different rates of missing values. Our experimental results show the effectiveness and efficiency of our proposed approach

Radio frequency traffic classification over WLAN

Network traffic classification is the process of analyzing traffic flows and associating them to different categories of network applications. Network traffic classification represents an essential task in the whole chain of network security. Some of the most important and widely spread applications of traffic classification are the ability to classify encrypted traffic, the identification of malicious traffic flows, and the enforcement of security policies on the use of different applications. Passively monitoring a network utilizing low-cost and low-complexity wireless local area network (WLAN) devices is desirable. Mobile devices can be used or existing office desktops can be temporarily utilized when their computational load is low. This reduces the burden on existing network hardware. The aim of this paper is to investigate traffic classification techniques for wireless communications. To aid with intrusion detection, the key goal is to passively monitor and classify different traffic types over WLAN to ensure that network security policies are adhered to. The classification of encrypted WLAN data poses some unique challenges not normally encountered in wired traffic. WLAN traffic is analyzed for features that are then used as an input to six different machine learning (ML) algorithms for traffic classification. One of these algorithms (a Gaussian mixture model incorporating a universal background model) has not been applied to wired or wireless network classification before. The authors also propose a ML algorithm that makes use of the well-known vector quantization algorithm in conjunction with a decision tree—referred to as a TRee Adaptive Parallel Vector Quantiser. This algorithm has a number of advantages over the other ML algorithms tested and is suited to wireless traffic classification. An average F-score (harmonic mean of precision and recall) > 0.84 was achieved when training and testing on the same day across six distinct traffic types