People-Centric Sensing Research Articles

Towards Developing Secure Data Aggregation with Integrity Verification Model (SDA-IV) in People Centric Sensing Systems

In present scenario of vast developments in wireless communication methods, embedded device based operations and mobile communications, sensor based techniques are widely adopted. Such systems are termed as People Centric Sensing Systems, which become very popular and acquires greater attention of researchers recently. However, security and privacy in transmitting data has been the major issue in People Centric Sensing Network (PCSN). For handling that problem efficiently, this paper presents a model called Security Data Aggregation and Integrity Verification (SDA-IV) model for providing privacy preserved data sharing between devices in PCSN. A new peer-to-peer oriented secure data sharing is achieved with the proposed model by making the user to shares their data randomly with other nodes along with the incorporating of integrity checking conceit. Moreover, the work comprises four phases: Initial Setup, Data Division and Encryption, Data Aggregation and Integrity Verification. Homomorphic Message Authentication Code is user for privacy preserving process and Hashing Functions are incorporated for integrity verification of shared data. Furthermore, the efficiency of the proposed SDA-IC model is evidenced using simulation results and comparative evaluations.

Distributionally Robust Semi-Supervised Learning for People-Centric Sensing

Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, humangenerated data inherently suffer from distribution shift in semi-supervised learning due to the diverse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.

Efficient privacy-preserving fault tolerance aggregation for people-centric sensing system

People-centric sensing (PCS) system is gaining popularity in the current technology world due to its ability to enhance the mobile device into a global mobile sensing device. But, PCS system is still suffering from security risks related to users privacy risks since the data being sensed by PCS are capable of allowing the attackers to gain privacy information related to the user. Hence, user privacy security is a main concern in the PCS system. In this paper, we propose to develop an efficient privacy-preserving fault tolerance aggregation technique for the PCS system. The proposed technique will consider registration of the involved mobile nodes and access point as an important initial step. Then, the data message being transmitted will be encrypted into reports and forwarded in a highly secure manner. Finally, the data will be decrypted and retrieved at the destination based on the homomorphic encryption and decryption mechanism. In this way, the privacy of the user is maintained secure and the process is made more tolerant toward fault in order to enhance efficient network operation. We evaluate the performance of the protocol according to the parameters like communication overhead, delay and delivery ratio.

Privacy Management and Optimal Pricing in People-Centric Sensing

With the emerging sensing technologies such as mobile crowdsensing and Internet of Things (IoT), people-centric data can be efficiently collected and used for analytics and optimization purposes. This data is typically required to develop and render people-centric services. In this paper, we address the privacy implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared to the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.

Design Perspectives of People Centric Sensing Systems

Mobile devices play a vital role in our day to day life. The various sensing modalities available in mobile devices are identified and justify the growth of the capabilities on today’s smart phones.Taking advantage of the sensing capabilities of the mobile devices paves way for a new era of network, referred as people centric sensing network. In this paper, a broad study about the applications, architectural components and the existing privacy and security architectures available are discussed. The applications of mobile sensing are categorized as personal, social and public sensing based on what they sense, how they share and infer. All these applications clearly depict the roles played by people. People may act as end users, as participants and as application administrators. Since people are in the loop, the basic privacy and security requirements are analyzed that can be realized as the need in the design of any mobile phone sensing systems. The possible threats that arise in this sensing context may be due to both internal and external entities. The strategies and types of adversarial models in the people centric sensing approach provides us light to how the framework works and solutions needed. Existing literature in terms of privacy preserving data aggregation schemes and security Frameworks are also discussed in detail. Applications: The model clearly portraits the importance of people who are no more only passive data users. More focus has to be given on privacy preserving models to make the system more acceptable by the people.

A survey on Privacy Preserving Data Aggregation Schemes in People Centric Sensing Systems and Wireless Domains

Background: The technological advances in mobile and wireless communication have paved way for a new era of large scale sensing network. Smart phones used by people are nowadays equipped with rich set of sensing capabilities. These mobile devices carried by people can be used together to form a network called people centric sensing network. Methods: Since people are the mobile custodians, privacy becomes a threat to the network. This paper focuses on a study about the various privacy preserving aggregation schemes existing in other domains of the wireless network including people centric sensing. The design goals and challenges that arise when attempting to protect the user’s sensitive data are analyzed. The techniques commonly used to achieve user’s privacy are the homomorphic property of encryption schemes, Data slicing and Mix schemes. Findings: A study about the existing privacy preserving aggregation schemes enables us to design a new method that is more suitable for people centric sensing network. The design considerations involved in developing a security and privacy framework are also identified. The privacy preserving aggregation scheme should support spatial and temporal aggregation with fault tolerance. The scheme has to be adaptable for both additive and non-additive statistical aggregation functions. Also maintaining the data integrity, data privacy and data accuracy with less computational and communication overhead is essential. Applications: Privacy protection is important for user’s participation which supports large set of cooperative sensing applications. Irrespective of various domains in wireless networks, the privacy preserving data aggregation model is discussed that is suitable for the context of the applications.

Special Issue on Advances in Trust, Security and Privacy for Wireless and Mobile Networks

The advances in wireless communication technologies and mobile devices have provided cost-effective platforms and environments for people to have better and ubiquitous interpersonal communications. The trust, security and privacy issues in such an environment have drawn significant attention recently from both academia and industry. The accepted papers in this special issue are devoted to the most recent developments and research addressing related theoretical and practical aspects on trust, security and privacy for wireless and mobile networks, and the contents are built on analytical modelling, experimental and simulation studies. The contributions of these papers are outlined below. People-centric sensing (PCS) is an emerging and promising paradigm of sensor networks, but faces severe security problems due to the strong connectivity and homogeneous applications. Lu et al. propose a novel behavioural signature generation system, called SimBehavior, to generate lightweight behavioural signature for malware detection in PCS. Unlike malware detection using behaviour graph which is NP-Complete, the proposed SimBehavior is efficient and suitable for malware detection in PCS. The experimental results show that SimBehavior can extract behavioural signatures effectively which can be used to detect new malware samples in PCS efficiently. Security and privacy protection have been the primary concerns in pushing towards the success of wireless mesh networks. In order to defend against the internal attacks and to achieve better security and privacy protection, Lin et al. propose a role based privacy-aware secure routing protocol (RPASRP), combining a new dynamic reputation mechanism with the role based multi-level security technology and a hierarchical key management protocol. Simulation results show that the proposed RPASRP implements the security and privacy

Generating Lightweight Behavioral Signature for Malware Detection in People-Centric Sensing

People-centric sensing (PCS) is an emerging paradigm of sensor network which turns daily used mobile devices (such as smartphones and PDAs) to sensors. It is promising but faces severe security problems. As smartphones are already and will keep up to be attractive targets to attackers, even more, with strong connectivity and homogeneous applications, all mobile devices in PCS will risk being infected by malware more rapidly. Even worse, attackers usually obfuscate their malwares in order to avoid simple (syntactic signature based) detection. Thus, more intelligent (behavioral signature based) detection is needed. But in the field of network security, the state-of-the-art behavioral signature--behavior graph--is too complicated to be used in mobile devices. This paper proposes a novel behavioral signature generation system--SimBehavior--to generate lightweight behavioral signature for malware detection in PCS. Generated lightweight behavioral signature is a bit like regex (regular expression) rules. And thus, unlike malware detection using behavior graph is NP-Complete, using our lightweight behavioral signature is efficient and very suitable for malware detection in PCS. Our experimental results show that SimBehavior can extract behavioral signatures effectively, and generated lightweight behavioral signatures can be used to detect new malware samples in PCS efficiently and effectively.

A privacy preserving authentication scheme with flexible identity revocation in people-centric sensing

People-Centric Sensing (PCS) is a new sensing paradigm, in which people’s context information is described by attributes. However, due to the nature of “people centric” and dynamicity, the prevalence of PCS faces two main challenges, including authentication with privacy preservation and complex identity revocation.In this paper, we propose the ABS-KR, an attribute-based signature scheme supporting key revocation. ABS-KR inherits the advantage of most attribute-based signature schemes, that is, a signature only shows that whether the user owns attributes satisfying the verification policy, no more information about his identifier or attributes, implementing authentication with privacy preservation. Furthermore, we introduce a mediator responsible for the key revocation to reduce the workload of the attribute authority managing users’ attributes and generating secret key for them. The core idea of performing flexible revocation is to divide a user’s secret key into two shares, and give one part to the mediator, making the user asking the mediator for help to generate a valid signature. Before answering the user, the mediator performs a revocation check for the user’s identifier and claimed attributes. If and only if the signer owns unrevoked attributes satisfying the policy, he can generate a valid signature. In addition, the mediator realizes instantaneous key revocation through revocation check during signing a message. Moreover, our ABS-KR supports monotone policies in the form of attribute trees under the computational Diffie–Hellman assumption.