A privacy-preserving user authentication mechanism for smart city mobile apps

In the dawn of the 5G era, the advances in mobile communications have created a suitable environment for the emergence of a wide spectrum of Smart City mobile applications that will play a significant role towards the improvement of citizens' life quality in the upcoming years. It is expected that Smart City mobile applications will allow citizens to access numerous and diverse services, provided by third-party Service Providers, through their mobile devices (e.g., smartphones). However, in order for the emerging Smart City mobile applications to gain the trust of all involved stakeholders and reach their full potential in the 5G market, many security and privacy concerns should be addressed in advance. Towards this direction, this paper proposes a privacy-preserving user authentication mechanism, developed in the context of the Mobilizador5G project, in order to: (a) provide mobile users with efficient and effective means to authenticate towards Service Providers, while preventing user identification and tracking; (b) allow anonymity revocation in case of user misbehavior; (c) avoid multiple user profiles creation on the Service Provider side; and (d) enable easy integration in current mobile application implementations

Secure multi-party computation-based privacy-preserving authentication for smart cities

The increasing concern for identity confidentiality in the Smart City scenario has fostered research on privacy-preserving authentication based on pseudonymization. Pseudonym systems enable citizens to generate pseudo-identities and establish unlinkable anonymous accounts in cloud service providers. The citizen's identity is concealed, and his/her different anonymous accounts cannot be linked to each other. Unfortunately, current pseudonym systems require a trusted certification authority (CA) to issue the cryptographic components (e.g. credentials, secret keys, or pseudonyms) to citizens. This CA, generally a Smart City governmental entity, has the capability to grant or revoke privacy rights at will, hence posing a serious threat in case of corruption. Additionally, if the pseudonym system enables de-anonymization of misusers, a corrupted CA can jeopardize the citizens' privacy. This paper presents a novel approach to construct a pseudonym system without a trusted issuer. The CA is emulated by a set of Smart City service providers by means of secure multi-party computation (MPC), which circumvents the requirement of assuming an honest CA. The paper provides a full description of the system, which integrates an MPC protocol and a pseudonym-based signature scheme. The system has been implemented and tested

A Revocable Group Signature Scheme with Scalability from Simple Assumptions and Its Application to Identity Management

Group signatures are signatures providing signer anonymity where signers can produce signatures on behalf of the group that they belong to. Although such anonymity is quite attractive considering privacy issues, it is not trivial to check whether a signer has been revoked or not. Thus, how to revoke the rights of signers is one of the major topics in the research on group signatures. In particular, scalability, where the signing and verification costs and the signature size are constant in terms of the number of signers N, and other costs regarding signers are at most logarithmic in N, is quite important. In this paper, we propose a revocable group signature scheme which is currently more efficient compared to previous all scalable schemes. Moreover, our revocable group signature scheme is secure under simple assumptions (in the random oracle model), whereas all scalable schemes are secure under q-type assumptions. We implemented our scheme by employing Barreto-Lynn-Scott curves of embedding degree 12 over a 455-bit prime field (BLS-12-455), and Barreto-Naehrig curves of embedding degree 12 over a 382-bit prime field (BN-12-382), respectively, by using the RELIC library. We showed that the online running times of our signing algorithm were approximately 14 msec (BLS-12-455) and 11 msec (BN-12-382), and those of our verification algorithm were approximately 20 msec (BLS-12-455) and 16 msec (BN-12-382), respectively. Finally, we showed that our scheme is applied to an identity management system proposed by Isshiki et al

Security and Privacy Preservation in Mobile Crowdsensing

Mobile crowdsensing (MCS) is a compelling paradigm that enables a crowd of individuals to cooperatively collect and share data to measure phenomena or record events of common interest using their mobile devices. Pairing with inherent mobility and intelligence, mobile users can collect, produce and upload large amounts of data to service providers based on crowdsensing tasks released by customers, ranging from general information, such as temperature, air quality and traffic condition, to more specialized data, such as recommended places, health condition and voting intentions. Compared with traditional sensor networks, MCS can support large-scale sensing applications, improve sensing data trustworthiness and reduce the cost on deploying expensive hardware or software to acquire high-quality data. Despite the appealing benefits, however, MCS is also confronted with a variety of security and privacy threats, which would impede its rapid development. Due to their own incentives and vulnerabilities of service providers, data security and user privacy are being put at risk. The corruption of sensing reports may directly affect crowdsensing results, and thereby mislead customers to make irrational decisions. Moreover, the content of crowdsensing tasks may expose the intention of customers, and the sensing reports might inadvertently reveal sensitive information about mobile users. Data encryption and anonymization techniques can provide straightforward solutions for data security and user privacy, but there are several issues, which are of significantly importance to make MCS practical. First of all, to enhance data trustworthiness, service providers need to recruit mobile users based on their personal information, such as preferences, mobility pattern and reputation, resulting in the privacy exposure to service providers. Secondly, it is inevitable to have replicate data in crowdsensing reports, which may possess large communication bandwidth, but traditional data encryption makes replicate data detection and deletion challenging. Thirdly, crowdsensed data analysis is essential to generate crowdsensing reports in MCS, but the correctness of crowdsensing results in the absence of malicious mobile users and service providers become a huge concern for customers. Finally yet importantly, even if user privacy is preserved during task allocation and data collection, it may still be exposed during reward distribution. It further discourage mobile users from task participation. In this thesis, we explore the approaches to resolve these challenges in MCS. Based on the architecture of MCS, we conduct our research with the focus on security and privacy protection without sacrificing data quality and users' enthusiasm. Specifically, the main contributions are, i) to enable privacy preservation and task allocation, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation. In SPOON, the service provider recruits mobile users based on their locations, and selects proper sensing reports according to their trust levels without invading user privacy. By utilizing the blind signature, sensing tasks are protected and reports are anonymized. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users; ii) to improve communication efficiency while guaranteeing data confidentiality, we propose a fog-assisted secure data deduplication scheme, in which a BLS-oblivious pseudo-random function is developed to enable fog nodes to detect and delete replicate data in sensing reports without exposing the content of reports. Considering the privacy leakages of mobile users who report the same data, the blind signature is utilized to hide users' identities, and chameleon hash function is leveraged to achieve contribution claim and reward retrieval for anonymous greedy mobile users; iii) to achieve data statistics with privacy preservation, we propose a privacy-preserving data statistics scheme to achieve end-to-end security and integrity protection, while enabling the aggregation of the collected data from multiple sources. The correctness verification is supported to prevent the corruption of the aggregate results during data transmission based on the homomorphic authenticator and the proxy re-signature. A privacy-preserving verifiable linear statistics mechanism is developed to realize the linear aggregation of multiple crowdsensed data from a same device and the verification on the correctness of aggregate results; and iv) to encourage mobile users to participating in sensing tasks, we propose a dual-anonymous reward distribution scheme to offer the incentive for mobile users and privacy protection for both customers and mobile users in MCS. Based on the dividable cash, a new reward sharing incentive mechanism is developed to encourage mobile users to participating in sensing tasks, and the randomization technique is leveraged to protect the identities of customers and mobile users during reward claim, distribution and deposit

Crowdsourcing atop blockchains

Traditional crowdsourcing systems, such as Amazon\u27s Mechanical Turk (MTurk), though once acquiring great economic successes, have to fully rely on third-party platforms to serve between the requesters and the workers for basic utilities. These third-parties have to be fully trusted to assist payments, resolve disputes, protect data privacy, manage user authentications, maintain service online, etc. Nevertheless, tremendous real-world incidents indicate how elusive it is to completely trust these platforms in reality, and the reduction of such over-reliance becomes desirable. In contrast to the arguably vulnerable centralized approaches, a public blockchain is a distributed and transparent global consensus computer that is highly robust. The blockchain is usually managed and replicated by a large-scale peer-to-peer network collectively, thus being much more robust to be fully trusted for correctness and availability. It, therefore, becomes enticing to build novel crowdsourcing applications atop blockchains to reduce the over-trust on third-party platforms. However, this new fascinating technology also brings about new challenges, which were never that severe in the conventional centralized setting. The most serious issue is that the blockchain is usually maintained in the public Internet environment with a broader attack surface open to anyone. This not only causes serious privacy and security issues, but also allows the adversaries to exploit the attack surface to hamper more basic utilities. Worse still, most existing blockchains support only light on-chain computations, and the smart contract executed atop the decentralized consensus computer must be simple, which incurs serious feasibility problems. In reality, the privacy/security issue and the feasibility problem even restrain each other and create serious tensions to hinder the broader adoption of blockchain. The dissertation goes through the non-trivial challenges to realize secure yet still practical decentralization (for urgent crowdsourcing use-cases), and lay down the foundation for this line of research. In sum, it makes the next major contributions. First, it identifies the needed security requirements in decentralized knowledge crowdsourcing (e.g., data privacy), and initiates the research of private decentralized crowdsourcing. In particular, the confidentiality of solicited data is indispensable to prevent free-riders from pirating the others\u27 submissions, thus ensuring the quality of solicited knowledge. To this end, a generic private decentralized crowdsourcing framework is dedicatedly designed, analyzed, and implemented. Furthermore, this dissertation leverages concretely efficient cryptographic design to reduce the cost of the above generic framework. It focuses on decentralizing the special use-case of Amazon MTurk, and conducts multiple specific-purpose optimizations to remove needless generality to squeeze performance. The implementation atop Ethereum demonstrates a handling cost even lower than MTurk. In addition, it focuses on decentralized crowdsourcing of computing power for specific machine learning tasks. It lets a requester place deposits in the blockchain to recruit some workers for a designated (randomized) programs. If and only if these workers contribute their resources to compute correctly, they would earn well-deserved payments. For these goals, a simple yet still useful incentive mechanism is developed atop the blockchain to deter rational workers from cheating. Finally, the research initiates the first systematic study on crowdsourcing blockchains\u27 full nodes to assist superlight clients (e.g., mobile phones and IoT devices) to read the blockchain\u27s records. This dissertation presents a novel generic solution through the powerful lens of game-theoretic treatments, which solves the long-standing open problem of designing generic superlight clients for all blockchains