Privacy-Preserving Accountable Cloud Storage

In cloud storage services, a wide range of sensitive information may be leaked to the host server via the exposure of access pattern albeit data is encrypted. Many security-provable schemes have been proposed to preserve the access pattern privacy; however, they may be vulnerable to attacks towards data integrity or availability from malicious users. This is due to the fact that, preserving access pattern privacy requires data to be frequently re-encrypted and re-positioned at the storage server, which can easily conceal the traces that are needed for account- ability support to detect misbehaviors and identify attackers. To address this issue, this paper proposes a scheme that integrates accountability support into hash-based ORAMs. Security analysis shows that the proposed scheme can detect misconduct committed by malicious users and identify the attackers, while preserving the access pattern privacy. Overhead analysis shows that the proposed accountability support incurs only slightly increased storage, communication, and computational overheads

An Accountability Scheme for Oblivious RAMs

In outsourced data services, revealing users’ data access pattern may lead to the exposure of a wide range of sensitive information even if data is encrypted. Oblivious RAM has been a well-studied provable solution to access pattern preservation. However, it is not resilient to attacks towards data integrity from the users or the server. In this paper, we study the problem of protecting access pattern privacy and data integrity together in outsourced data services, and propose a scheme that introduces accountability support into a hash-based ORAM design. The proposed scheme can detect misconduct committed by malicious users or server, and identify the attacker, while not interfering with the access pattern preservation mechanisms inherent from the underlying ORAM. This is accomplished at the cost of slightly increased computational, storage, and communication overheads compared with the original ORAM

Barrier information coverage with wireless sensors

Abstract—Sensor networks have been deployed for many barrier coverage applications such as intrusion detection and border surveillance. In these applications, it is critical to operate a sensor network in an energy-efficient manner so the barrier can be covered with as few active sensors as possible. In this paper, we study barrier information coverage which exploits collaborations and information fusion between neighboring sensors to reduce the number of active sensors needed to cover a barrier and hence to prolong the network lifetime. Moreover, we propose a practical solution to identify the barrier information coverage set which can information-cover the barrier with a small number of active sensors. The effectiveness of the proposed solution is demonstrated by numerical and simulation results. I

Dependability issues with ubiquitous wireless access

Recent years have witnessed a proliferation of the number of wireless technologies available to access the Internet, ranging from wireless local area networks to cellular and broadcast systems, and ad hoc and mesh networks. While the emergence of these new technologies can enable truly ubiquitous Internet access, it also raises issues with the dependability of the Internet service delivered to users. Dependability in this context refers to the ability of a wireless access system to deliver specified services on which users can rely.European Community's Seventh Framework ProgramPublicad

D3S: A Framework for Enabling Unmanned Aerial Vehicles as a Service

In this paper, we consider the use of UAVs to provide wireless connectivity services, for example after failures of wireless network components or to simply provide additional bandwidth on demand, and introduce the concept of UAVs as a service (UaaS). To facilitate UaaS, we introduce a novel framework, dubbed D3S, which consists of four phases: demand, decision, deployment, and service. The main objective of this framework is to develop efficient and realistic solutions to implement these four phases. The technical problems include determining the type and number of UAVs to be deployed, and also their final locations (e.g., hovering or on-ground), which is important for serving certain applications. These questions will be part of the decision phase. They also include trajectory planning of UAVs when they have to travel between charging stations and deployment locations and may have to do this several times. These questions will be part of the deployment phase. The service phase includes the implementation of the backbone communication and data routing between UAVs and between UAVs and ground control stations

A Comprehensive and Reliable Feature Attribution Method: Double-sided Remove and Reconstruct (DoRaR)

The limited transparency of the inner decision-making mechanism in deep neural networks (DNN) and other machine learning (ML) models has hindered their application in several domains. In order to tackle this issue, feature attribution methods have been developed to identify the crucial features that heavily influence decisions made by these black box models. However, many feature attribution methods have inherent downsides. For example, one category of feature attribution methods suffers from the artifacts problem, which feeds out-of-distribution masked inputs directly through the classifier that was originally trained on natural data points. Another category of feature attribution method finds explanations by using jointly trained feature selectors and predictors. While avoiding the artifacts problem, this new category suffers from the Encoding Prediction in the Explanation (EPITE) problem, in which the predictor's decisions rely not on the features, but on the masks that selects those features. As a result, the credibility of attribution results is undermined by these downsides. In this research, we introduce the Double-sided Remove and Reconstruct (DoRaR) feature attribution method based on several improvement methods that addresses these issues. By conducting thorough testing on MNIST, CIFAR10 and our own synthetic dataset, we demonstrate that the DoRaR feature attribution method can effectively bypass the above issues and can aid in training a feature selector that outperforms other state-of-the-art feature attribution methods. Our code is available at https://github.com/dxq21/DoRaR.Comment: 16 pages, 22 figure

MU-ORAM: Dealing with Stealthy Privacy Attacks in Multi-User Data Outsourcing Services

Outsourcing data to remote storage servers has become more and more popular, but the related security and privacy concerns have also been raised. To protect the pattern in which a user accesses the outsourced data, various oblivious RAM (ORAM) constructions have been designed. However, when existing ORAM designs are extended to support multi-user scenarios, they become vulnerable to stealthy privacy attacks targeted at revealing the data access patterns of innocent users, even if only one curious or compromised user colludes with the storage server. To study the feasibility and costs of overcoming the above limitation, this paper proposes a new ORAM construction called Multi-User ORAM (MU-ORAM), which is resilient to stealthy privacy attacks. The key ideas in the design are (i) introduce a chain of proxies to act as a common interface between users and the storage server, (ii) distribute the shares of the system secrets delicately to the proxies and users, and (iii) enable a user and/or the proxies to collaboratively query and shuffle data. Through extensive security analysis, we quantify the strength of MU-ORAM in protecting the data access patterns of innocent users from attacks, under the assumption that the server, users, and some but not all proxies can be curious but honest, compromised and colluding. Cost analysis has been conducted to quantify the extra overhead incurred by the MU-ORAM design