As a result of the information explosion and the development of the technology of the Internet of Things (IoT), tons of data are collected and transmitted, some of which may concern private or confidential information...
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As a result of the information explosion and the development of the technology of the Internet of Things (IoT), tons of data are collected and transmitted, some of which may concern private or confidential information. Sensors, as the building blocks of such processes, are commonly resource-limited. For instance, they rarely possess large storage space, abundant electricity reserves, or sufficient computing power. So, how to improve the efficiency of data acquisition and transmission without trimming security has attracted much attention from researchers. Recently, compressive sensing (CS) theory has been exploited to improve the efficiency of data collection and transmission, while there are some drawbacks to existing CS-based data collection and transmission schemes. For instance, many existing CS-based data transmission methods lack data encryption or privacy protection mechanisms, meaning that private or confidential information may be exposed to uncertainty if such data are captured during transmission. This article proposes a method of collecting and transmitting datasecurely and efficiently based on P-tensor product (PTP) CS and secret key encryption mechanism. It could provide data security under the assumption that participants and dataprocessing domains are trusted. And adversaries could not obtain useful information, even if they manage to capture data that are transmitted. Compared with most existing compressive-sensing-based methods, the proposed method addresses privacy protection issues by realizing multilevel critical information concealment. By reducing the scale of measurement matrices, it achieves lower computing and storage resource consumption. To transmit data more securely, it also provides an encryption mechanism with sufficient key space and high key sensitivity. Overall, the proposed method is secure, energy-efficient, and provides privacy protection functions, which makes it particularly suitable for IoT sensor nodes with restricted comp
In this paper, we provide a thorough literature review on the field of integer homomorphic encryption when it is incorporated into FPGAs. Homomorphic encryption allows one to perform operations on ciphertexts and get ...
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In this paper, we propose a secure protocol that allows processing encrypted data emitted by an IOT device with low computational capabilities. Its originality is threefold. It first relies on a new fast algorithm whi...
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In this paper, we propose a secure protocol that allows processing encrypted data emitted by an IOT device with low computational capabilities. Its originality is threefold. It first relies on a new fast algorithm which makes possible the conversion of Combined Linear Congruential Generator (CLCG) encrypted IOT data into data homomorphically encrypted with the Damgard-Jurik (D-J) cryptosystem. In second, an original data packing strategy is given so as to reduce communication and computation complexity as well as process several D-J encrypted data at once by means of matrix operations. In third, we introduce a crypto-watermarking based integrity control mechanism. This one combines the lightweight hash function Quark with LSB substitution so as to offer the capability to check the integrity of CLCG encrypted data. We illustrate the deployment of our protocol, in the case an honest-but-curious or malicious third party wants to process encrypted data issued from a real connected knee prosthesis. We theoretically and experimentally demonstrate the performance of our solution. This one can nearly process 500 samples every second. Beyond, our proposal is suited to the general case of IOT.
This article considers the ensuring of secure data processing in distributed computer systems (DCSs), which is important for a certain class of computing tasks. An approach to the resource management in DCSs is propos...
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This article considers the ensuring of secure data processing in distributed computer systems (DCSs), which is important for a certain class of computing tasks. An approach to the resource management in DCSs is proposed that makes it possible to take into account, according to user requirements, both the time spent on the execution of a task and the security level of the system resources involved in its execution.
Cloud computing needs to provide integrity assurance in order to support security sensitive application services such as critical dataflow processing. In this paper, we present a novel RObust Service Integrity Attesta...
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ISBN:
(纸本)9781450302449
Cloud computing needs to provide integrity assurance in order to support security sensitive application services such as critical dataflow processing. In this paper, we present a novel RObust Service Integrity Attestation (ROSIA) framework that can efficiently verify the integrity of stateful dataflow processing services and pinpoint malicious service providers within a large-scale cloud system. ROSIA achieves robustness by supporting stateful dataflow services such as windowed stream operators, and performing integrated consistency check to detect colluding attacks. We have implemented ROSIA on top of the IBM System S dataflow processing system and tested it on the NCSU virtual computing lab. Our experimental results show that our scheme is feasible and efficient for large-scale cloud systems.
This article examines the potential of the Cell processor as a platform for securedata mining on the future volunteer computing systems. Volunteer computing platforms have the potential to provide massive computing p...
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This article examines the potential of the Cell processor as a platform for securedata mining on the future volunteer computing systems. Volunteer computing platforms have the potential to provide massive computing power. However, privacy and security concerns prevent using volunteer computing for data mining of sensitive data. The Cell processor comes with hardware security features. The secure volunteer data mining can be achieved by using those hardware security features. In this article, we present a general security scheme for the volunteer computing, and a secure parallelized K-Means clustering algorithm for the Cell processor. We also evaluate the performance of the algorithm on the Cell secure system simulator. Evaluation results indicate that the proposed securedata clustering outperforms a non-secure clustering algorithm on the general purpose CPU, but incurs a huge performance overhead introduced by the decryption process of the Cell security features. Possible optimization for the secure K-Means clustering is discussed.
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