/pdf/sec-x-2-recommended-elliptic-curve-domain-parameters-54wojro6fg.pdf

SEC X.2: Recommended Elliptic Curve Domain Parameters

Distributed sensor data storage and retrieval have gained increasing popularity in recent years for supporting various applications. While distributed architecture enjoys a more robust and fault-tolerant wireless sensor network (WSN), such architecture also poses a number of security challenges especially when applied in mission-critical applications such as battlefield and e-healthcare. First, as sensor data are stored and maintained by individual sensors and unattended sensors are easily subject to strong attacks such as physical compromise, it is significantly harder to ensure data security. Second, in many mission-critical applications, fine-grained data access control is a must as illegal access to the sensitive data may cause disastrous results and/or be prohibited by the law. Last but not least, sensor nodes usually are resource-constrained, which limits the direct adoption of expensive cryptographic primitives. To address the above challenges, we propose, in this paper, a distributed data access control scheme that is able to enforce fine-grained access control over sensor data and is resilient against strong attacks such as sensor compromise and user colluding. The proposed scheme exploits a novel cryptographic primitive called attribute-based encryption (ABE), tailors, and adapts it for WSNs with respect to both performance and security requirements. The feasibility of the scheme is demonstrated by experiments on real sensor platforms. To our best knowledge, this paper is the first to realize distributed fine-grained data access control for WSNs.

FDAC: Toward Fine-Grained Distributed Data Access Control in Wireless Sensor Networks

Review: Security in wireless ad-hoc networks - A survey

Online data storage using implicit security

Recently, distributed data storage has gained increasing popularity for efficient and robust data management in wireless sensor networks (WSNs). The distributed architecture makes it challenging to build a highly secure and dependable yet lightweight data storage system. On the one hand, sensor data are subject to not only Byzantine failures, but also dynamic pollution attacks, as along the time the adversary may modify/pollute the stored data by compromising individual sensors. On the other hand, the resource-constrained nature of WSNs precludes the applicability of heavyweight security designs. To address the challenge, in this article we propose a novel dependable and secure data storage scheme with dynamic integrity assurance. Based on the principle of secret sharing and erasure coding, we first propose a hybrid share generation and distribution scheme to achieve reliable and fault-tolerant initial data storage by providing redundancy for original data components. To further dynamically ensure the integrity of the distributed data shares, we then propose an efficient data integrity verification scheme exploiting the techniques of algebraic signature and spot-checking. The proposed scheme enables individual sensors to verify in one protocol execution the correctness of all the pertaining data shares simultaneously in the absence of the original data. Extensive security analysis shows that the proposed scheme has strong resistance against various data pollution attacks. The efficiency of the scheme is demonstrated by experiments on sensor platforms Tmote Sky and iMote2.

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Dependable and Secure Sensor Data Storage with Dynamic Integrity Assurance

Numerous authentication schemes have been proposed in the past for protecting communication authenticity and integrity in wireless sensor networks. Most of them however have following limitations: high computation or communication overhead, no resilience to a large number of node compromises, delayed authentication, lack of scalability, etc. To address these issues, we propose in this paper a novel message authentication approach which adopts a perturbed polynomial-based technique to simultaneously accomplish the goals of lightweight, resilience to a large number of node compromises, immediate authentication, scalability, and non-repudiation. Extensive analysis and experiments have also been conducted to evaluate the scheme in terms of security properties and system overhead.

Lightweight and Compromise-Resilient Message Authentication in Sensor Networks

Sensor networks have been an attractive platform for pervasive computing and communication Due to the lack of physical protection, however, sensor networks are vulnerable to attacks if deployed in hostile environments When a sensor network is under attack, the most fundamental concern is that information communicated or stored in the network remains safe The past research has focused on securing information in communication, but how to secure information in storage has been generally overlooked Meanwhile, distributed data storage and retrieval have become popular for efficient data management in sensor networks, which renders the absence of schemes for securing stored information to be a more severe problem Hence, we propose in this paper three schemes to deal with the problem All the schemes have the following properties: (i) only authorized entities can access data stored in the sensor network; (ii) the schemes are resilient to a large number of sensor node compromises The second and the third schemes do not involve any centralized entity except for a few initialization or renewal operations, and thus support secure, distributed data storage and retrieval The third scheme further provides high scalability and flexibility, and hence is most suitable in real applications The effectiveness and efficiency of the proposed schemes have also been verified through extensive analysis and TOSSIM-based simulations

Securing Distributed Data Storage and Retrieval in Sensor Networks

Mobile devices like laptops, iPhones and PDAs are highly susceptible to theft in public places like airport terminal, library and cafe. Moreover, the exposure of sensitive data stored in the mobile device could be more damaging than the loss of device itself. In this work, we propose and implement a pervasive mobile device protection system, named EagleVision, based on sensing and wireless networking technologies. Comparing with existing solutions, EagleVision is unique in providing an integrated protection to both device and data. It is a context-aware system which adjusts the protection level to the mobile device dynamically according to the context information such as the user proximity to the mobile device, which is collected via the interactions between the sensors carried by the user, embedded with the mobile device and deployed in the surrounding environment. Furthermore, it does not require explicit user intervention to utilize the system and hence avoid adding extra distractions to the user. Prototype implementation and extensive field test results demonstrate the effectiveness of EagleVision.

/pdf/eaglevision-a-pervasive-mobile-device-protection-system-49g8zi74ga.pdf

EagleVision: A pervasive mobile device protection system

With in-network sensor data storage and query, storage nodes are responsible for storing the data collected by sensor nodes and answering queries from users. Thus, without proper protection for data types and user queries, compromise of storage nodes and/or sensor nodes may reveal sensitive infor- mation about the sensed environment as well as users' private interests and query patterns. In this paper, we explore trade-offs between privacy, computation overhead, communication overhead, network flexibility and network complexity, and propose ElliPS (Elliptic curve based Privacy Scheme) to provide joint protection on data type privacy and query privacy in the presence of sensor node compromise, storage node compromise, or under collusive attacks by compromised sensor nodes and storage nodes together. Extensive analysis and simulation are conducted to verify the security properties and efficiency of the proposed scheme.

/pdf/ellips-a-privacy-preserving-scheme-for-sensor-data-storage-2doiwimgcb.pdf

ElliPS: A Privacy Preserving Scheme for Sensor Data Storage and Query

Information System is one of the key domain in which lot of research has been done in the past few years. Applying Information systems to heterogeneous and distributed environments is one of the current research areas. Business Process Management Systems is the information systems which deal with the administration of tasks in the business processes, organizational structures, or in the related context. The Workflow Management system, the early idea of BPM, controls the workflow in an organization, data transfer, and integration of legacy information systems with existing programs and program modules, delegation of business tasks. Offering task management services especially modeling of business processes and underlying organizations, BPM serves it meaning and incorporates the WFM system. BPM supporting the use of knowledge concerning the awareness/unawareness of integrated software, and analysis of processes and organizational structure in terms of verification, modification, evaluation are key management in BPM system I.INTRODUCTION TO BPM: Combination of corporate restructuring and mergers urged the department and processes to be automated and linked together. Unfortunately it created largely disconnected spaghetti of systems and applications. Business Process Management (BPM) was born from a strong need to streamline internal processes and connections between both internal and external functions. It provides ability to model, monitor, manage and manipulate processes quickly in response to changes in strategy and market forces. BPM is an extension of classical Work Flow Management (WFM) systems and approaches. BPM delivers the maximum utility of legacy system. It is a method for revolutionizing the way the work moves throughout an organization. II.COMPONENTS OF BPM: The four key elements in Business Process Management are: Process modeling, Process rule, Process management, and Process performance analytics. Process modeling: Process modeling describes the flow of information (business process).It helps in building a model of process and mapping into an implementation frame work. Modeling requires a consistent analysis and representation of the processes. It forms the basis for the userspecific process environment. Process Rule: Process Rule is a heart of the BPM. It involves execution of discrete steps & integration of information. A process rules engine is used for this purpose. The engine is to monitor and manage the given steps by providing a flow control over the business process. If a process fails or delayed, a compensated action can be done to rectify. Process Management: Process Management is to correlate business/application events to fundamental business metrics .It enables the organization to monitor and optimize the business process. Once the process has been analyzed, modeled and the associated rules are formed. The next stage involves the visualization and management. Visualization means transaction data ranging from holistic to process-state or client state specific rules. Process Performance Analytics: Process Performance Analytics is a transition data source available for sophisticated management analytics .This data should be available on a retrospective or real-time basis. It should answer to one of the most difficult question like “how much does it cost to process a specific transaction?” By this way BPM mainly addresses four components. They are Work Flow Management, Content and records management Enterprise Application Integration (EAI) and Business Activity monitoring (BAM). III.RELEVENCE TO SYSTEM INTEGRATION: BPM can be fitted easily on the top of existing IT system. It is the Process rules that governs all the happenings and optimizes the process. BPM will not be able to work without the connectivity provided by the EAI layer. EAI layer provides BPM to gain and measure the value from the investment. On the other hand, BPM provides value to the EAI technology layer of Information technology. BPM layer offers business management the view of the message processing layer. It is used to unlock the value of the information held in the integration layer. It is also used to gather data of every transaction in the lifecycle. IV.APPLICATION OF BPM: BPM can be applied to any application which needs these advantages. BPM reduces unnecessary high losses in time critical transactions, helps to provide any critical information, practical application yield fast ROI ,better control over the information, identifying and maximizing the benefits faster, better analysis and tracking, provides enterprise-wide visibility of business processes, improve the effectiveness of the core operations, provides ability to coordinate interactions between information systems, business process and the people, flexible and easily changed by non-technical business people.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1254&context=cs_techreports

N. Subramanian

Papers

Lightweight and Compromise-Resilient Message Authentication in Sensor Networks

Securing Distributed Data Storage and Retrieval in Sensor Networks

EagleVision: A pervasive mobile device protection system

ElliPS: A Privacy Preserving Scheme for Sensor Data Storage and Query

Business Process Management