Participatory sensing is emerging as an innovative computing paradigm that targets the ubiquity of always-connected mobile phones and their sensing capabilities. In this context, a multitude of pioneering applications increasingly carry out pervasive collection and dissemination of information and environmental data, such as, traffic conditions, pollution, temperature, etc. Participants collect and report measurements from their mobile devices and entrust them to the cloud to be made available to applications and users. Naturally, due to the personal information associated to the reports (e.g., location, movements, etc.), a number of privacy concerns need to be taken into account prior to a large-scale deployment of these applications. Motivated by the need for privacy protection in Participatory Sensing, this work presents PEPSI: a Privacy-Enhanced Participatory Sensing Infrastructure. We explore realistic architectural assumptions and a minimal set of formal requirements aiming at protecting privacy of both data producers and consumers. We propose two instantiations that attain privacy guarantees with provable security at very low additional computational cost and almost no extra communication overhead.
Both @cite_17 and @cite_40 guarantee report integrity using group signatures (i.e., all sensors share the same group key to sign reports) and provide limited confidentiality, as reports are encrypted under the public key of a , a trusted party responsible for collecting reports and distributing them to queriers.
@cite_40 @cite_17
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A number of multi-hop, wireless, network programming systems have emerged for sensor network retasking but none of these systems support a cryptographically-strong, public-key-based system for source authentication and integrity verification. The traditional technique for authenticating a program binary, namely a digital signature of the program hash, is poorly suited to resource-contrained sensor nodes. Our solution to the secure programming problem leverages authenticated streams, is consistent with the limited resources of a typical sensor node, and can be used to secure existing network programming systems. Under our scheme, a program binary consists of several code and data segments that are mapped to a series of messages for transmission over the network. An advertisement, consisting of the program name, version number, and a hash of the very first message, is digitally signed and transmitted first. The advertisement authenticates the first message, which in turn contains a hash of the second message. Similarly, the second message contains a hash of the third message, and so on, binding each message to the one logically preceding it in the series through the hash chain. We augmented the Deluge network programming system with our protocol and evaluated the resulting system performance. A major issue in many applications of Wireless Sensor Networks (WSNs) is ensuring security. Particularly, in military applications, sensors are usually deployed in hostile areas where they can be easily captured and operated by an adversary. Most of security attacks in WSNs are due to the lack of security guaranties in terms of authentication, integrity, and confidentiality. These services are often provided using cryptographic primitives where sensor nodes need to agree on a set of secret keys. Current key distribution schemes are not fully adapted to the tiny, low-cost, and fragile nature of sensors that are equipped with limited computation capability, reduced memory size, and battery-based power supply. This paper investigates the design of an efficient key distribution and management scheme for wireless sensor networks. The proposed scheme can ensure the generation and distribution of different encryption keys intended to secure individual and group communications. This is performed based on elliptic curve public key encryption using Diffie-Hellman like key exchange that is applied at different levels of the network topology. In addition, a re-keying procedure is performed using secret sharing techniques. This scheme is more efficient and less complex than existing approaches, due to the reduced number of messages and the less processing overhead required to accomplish key exchange. Furthermore, few number of encryption keys with reduced sizes are managed in sensor nodes, which optimizes memory usage and enhances scalability to large size networks. Commodity mobile devices have been utilized as sensor nodes in a variety of domains, including citizen journalism, mobile social services, and domestic eldercare. In each of these domains, data integrity and device-owners' privacy are first-class concerns, but current approaches to secure sensing fail to balance these properties. External signing infrastructure cannot attest to the values generated by a device's sensing hardware, while trusted sensing hardware does not allow users to securely reduce the fidelity of readings in order to preserve their privacy. In this paper we examine the challenges posed by the potentially conflicting goals of data integrity and user privacy and propose a trustworthy mobile sensing platform which leverages inexpensive commodity Trusted Platform Module (TPM) hardware. Sensor networks include nodes with limited computation and communication capabilities. One of the basic functions of sensor networks is to sense and transmit data to the end users. The resource constraints and security issues pose a challenge to information aggregation in large sensor networks. Bootstrapping keys is another challenge because public key cryptosystems are unsuitable for use in resource-constrained sensor networks. In this paper, we propose a solution by dividing the problem in two domains. First, we present a protocol for establishing cluster keys in sensor networks using verifiable secret sharing. We chose elliptic curve cryptosystems for security because of their smaller key size, faster computations and reductions in processing power. Second, we develop a secure data aggregation and verification (SecureDAV) protocol that ensures that the base station never accepts faulty aggregate readings. An integrity check of the readings is done using Merkle hash trees, avoiding over-reliance on the cluster-heads.