AnonyControl: Control Cloud Data Anonymously with Multi-Authority Attribute-Based Encryption

Cloud computing is a revolutionary computing paradigm which enables flexible, on-demand and low-cost usage of computing resources. However, those advantages, ironically, are the causes of security and privacy problems, which emerge because the data owned by different users are stored in some cloud servers instead of under their own control. To deal with security problems, various schemes based on the Attribute- Based Encryption (ABE) have been proposed recently. However, the privacy problem of cloud computing is yet to be solved. This paper presents an anonymous privilege control scheme AnonyControl to address the user and data privacy problem in a cloud. By using multiple authorities in cloud computing system, our proposed scheme achieves anonymous cloud data access, finegrained privilege control, and more importantly, tolerance to up to (N -2) authority compromise. Our security and performance analysis show that AnonyControl is both secure and efficient for cloud computing environment.Comment: 9 pages, 6 figures, 3 tables, conference, IEEE INFOCOM 201

Authentication and key establishment in wireless networks

Ph.DDOCTOR OF PHILOSOPH

Raman piezospectroscopic evaluation of intergrowth ferroelectric polycrystalline ceramic in biaxial bending configuration

The piezospectroscopic (PS) effect was studied in an intergrowth bismuth layer-structure ferroelectricceramicBi₅TiNbWO₁₅ according to a micro-Raman spectroscopic evaluation. By using a ball-on-ring flexure configuration, a biaxial stress was generated in a Bi₅TiNbWO₁₅ plate-like specimen and in situ collected Raman spectra were acquired and analyzed under several loading conditions. As the observed spectral line contained signals arising from the whole illuminated in-depth region, the laser probe information was deconvoluted (by means of an in-depth probe response function obtained according to the defocusing method) in order to deduce biaxial PS coefficients for the three Raman bands of Bi₅TiNbWO₁₅ located at 763, 857, and 886 cm−1, respectively. The biaxial PS coefficients of these bands were derived to be −1.74±0.16, −2.51±0.16, and −2.64±0.31 cm⁻¹/GPa, respectively, and should be referred to the c axis of the Bi5TiNbWO15 crystal

ContactChaser: A Simple yet Effective Contact Tracing Scheme with Strong Privacy

The COVID-19 pandemic is a severe threat to both lives and economics throughout the world. Advanced information technology can play an important role to win this war against this invisible enemy. The most effective way to fight COVID-19 is quarantining infected people and identifying their contacts. Recently, quite a few Bluetooth-based contact tracing proposals have been proposed to identify who has come into contact with infected people. The success of a contact tracing system depends on multiple factors, including security and privacy features, simplicity and user-friendliness etc. More importantly, it should help the health authority to effectively enforce contact tracing, so as to control spreading of the vital virus as soon as possible. However, current proposals are either susceptible to security and privacy attacks, or expensive in computation and/or communication costs. In this paper, we propose ContactChaser, a simple but effective contact tracing scheme based on group signature, to achieve strong security and privacy protection for users. ContactChaser only requires a health authority to issue group private keys to users for only once, without frequently updating keys with the authority. It helps the authority to find out the close contacts of infected people, but just leaks the minimum information necessary for contact tracing to the health authority. Specially, the contact relationship is protect against the authority, which only knows the close contacts of infected people. ContactChaser is able to prevent most attacks, especially relay and replay attacks, so that it can effectively avoid false alerts and reduce unreported contacts. We give a detailed analysis of ContactChaser’s security and privacy properties as well as its performance. It is expected ContactChaser can contribute to the design and development of contact tracing schemes

Not Every Couple Is a Pair: A Supervised Approach for Lifetime Collaborator Identification

While scientific collaboration can be critical for a scholar, some collaborator(s) can be more significant than others, a.k.a. lifetime collaborator(s). This work-in-progress aims to investigate whether it is possible to predict/identify lifetime collaborators given a junior scholar\u27s early profile. For this purpose, we propose a supervised approach by leveraging scholars\u27 local and network properties. Extensive experiments on DBLP digital library demonstrate that lifetime collaborators can be accurately predicted. The proposed model outperforms baseline models with various predictors. Our study may shed light on the exploration of scientific collaborations from the perspective of life-long collaboration

HIBEChain: A Hierarchical Identity-based Blockchain System for Large-Scale IoT

Internet-of-Things enables interconnection of billions of devices, which perform autonomous operations and collect various types of data. These things, along with their generated huge amount of data, need to be handled efficiently and securely. Centralized solutions are not desired due to security concerns and scalability issue. In this paper, we propose HIBEChain, a hierarchical blockchain system that realizes scalable and accountable management of IoT devices and data. HIBEChain consists of multiple permissioned blockchains that form a hierarchical tree structure. To support the hierarchical structure of HIBEChain, we design a decentralized hierarchical identity-based signature (DHIBS) scheme, which enables IoT devices to use their identities as public keys. Consequently, HIBEChain achieves high scalability through parallel processing as blockchain sharding schemes, and it also implements accountability by use of identity-base keys. Identity-based keys not only make HIBEChain more user-friendly, they also allow private key recovery by validators when necessary. We provide detailed analysis of its security and performance, and implement HIBEChain based on Ethereum source code. Experiment results show that a 6-ary, (7,10)-threshold, 4-level HIBEChain can achieve 32,000 TPS, and it needs only 9 seconds to confirm a transaction

SuperFL: Privacy-Preserving Federated Learning with Efficiency and Robustness

Federated Learning (FL) accomplishes collaborative model training without the need to share local training data. However, existing FL aggregation approaches suffer from inefficiency, privacy vulnerabilities, and neglect of poisoning attacks, severely impacting the overall performance and reliability of model training. In order to address these challenges, we propose SuperFL, an efficient two-server aggregation scheme that is both privacy preserving and secure against poisoning attacks. The two semi-honest servers $\mathcal{S}_0$ and $\mathcal{S}_1$ collaborate with each other, with a shuffle server $\mathcal{S}_0$ in charge of privacy-preserving random clustering, while an analysis server $\mathcal{S}_1$ responsible for robustness detection, identifying and filtering malicious model updates. Our scheme employs a novel combination of homomorphic encryption and proxy re-encryption to realize secure server-to-server collaboration. We also utilize a novel sparse matrix projection compression technique to enhance communication efficiency and significantly reduce communication overhead. To resist poisoning attacks, we introduce a dual-filter algorithm based on trusted root, combine dimensionality reduction and norm calculation to identify malicious model updates. Extensive experiments validate the efficiency and robustness of our scheme. SuperFL achieves impressive compression ratios, ranging from $5\text{-}40$x, under different models while maintaining comparable model accuracy as the baseline. Notably, our solution demonstrates a maximal model accuracy decrease of no more than $2\%$ and $6\%$ on the MNIST and CIFAR-10 datasets respectively, under specific compression ratios and the presence of malicious clients

Division of Regulatory Power: Collaborative Regulation for Privacy-Preserving Blockchains

Decentralized anonymous payment schemes may be exploited for illicit activities, such as money laundering, bribery and blackmail. To address this issue, several regulatory friendly decentralized anonymous payment schemes have been proposed. However, most of these solutions lack restrictions on the regulator’s authority, which could potentially result in power abuse and privacy breaches. In this paper, we present a decentralized anonymous payment scheme with collaborative regulation (DAPCR). Unlike existing solutions, DAPCR reduces the risk of power abuse by distributing regulatory authority to two entities: Filter and Supervisor, neither of which can decode transactions to access transaction privacy without the assistance of the other one. Our scheme enjoys three major advantages over others: ① Universality, achieved by using zk-SNARK to extend privacy-preserving transactions for regulation. ② Collab orative regulation, attained by adding the ring signature with controllable linkability to the transaction. ③ Efficient aggregation of payment amounts, achieved through amount tags. As a key technology for realizing collaborative regulation in DAPCR, the ring signature with controllable linkability (CLRS) is proposed, where a user needs to specify a linker and an opener to generate a signature. The linker can extract pseudonyms from signatures and link signatures submitted by the same signer based on pseudonyms, without leaking the signer’s identity. The opener can recover the signer’s identity from a given pseudonym. The experimental results reflect the efficiency of DAPCR. The time overhead for transaction generation is 1231.2 ms, representing an increase of less than 50 % compared to ZETH. Additionally, the time overhead for transaction verification is only 1.2 ms