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Storage-as-a-service (STaaS) permits the client to outsource her data to the cloud, thereby reducing data management and maintenance costs. However, STaaS also brings significant data integrity and soundness concerns since the storage provider might not keep the client data intact and retrievable all the time (e.g., cost saving via deletions). Proof of Retrievability (PoR) can validate the integrity and retrievability of remote data effectively. This technique can be useful for regular audits to monitor data compromises, as well as to comply with standard data regulations. In particular, cold storage applications (e.g., MS Azure, Amazon Glacier) require regular and frequent audits with less frequent data modification. Yet, despite their merits, existing PoR techniques generally focus on other metrics (e.g., low storage, fast update, metadata privacy) but not audit efficiency (e.g., low audit time, small proof size). Hence, there is a need to develop new PoR techniques that achieve efficient data audit while preserving update and retrieval performance. In this paper, we propose Porla, a new PoR framework that permits efficient data audit, update, and retrieval functionalities simultaneously. Porla permits data audit in both private and public settings, each of which features asymptotically (and concretely) smaller audit-proof size and lower audit time than all the prior works while retaining the same asymptotic data update overhead. Porla achieves all these properties by composing erasure codes with verifiable computation techniques which, to our knowledge, is a new approach to PoR design. We address several challenges that arise in such a composition by creating a new homomorphic authenticated commitment scheme, which can be of independent interest. We fully implemented Porla and evaluated its performance on commodity cloud (i.e., Amazon EC2) under various settings. Experimental results demonstrated that Porla achieves two to four orders of magnitude smaller audit proof size with 4x–18000x lower audit time than all prior schemes in both private and public audit settings at the cost of only 2x–3x slower update. 

End-to-end encrypted file-sharing systems enable users to share files without revealing the file contents to the storage servers. However, the servers still learn metadata, including user identities and access patterns. Prior work tried to remove such leakage but relied on strong assumptions. Metal (NDSS '20) is not secure against malicious servers. MCORAM (ASIACRYPT '20) provides confidentiality against malicious servers, but not integrity. Titanium is a metadata-hiding file-sharing system that offers confidentiality and integrity against malicious users and servers. Compared with MCORAM, which offers confidentiality against malicious servers, Titanium also offers integrity. Experiments show that Titanium is 5x-200x faster or more than MCORAM. 

Proof-of-Work (PoW) is one of the fundamental and widely-used consensus algorithms in blockchains. In PoW, nodes compete to receive the mining reward by trying to be the first to solve a puzzle. Despite its fairness and wide availability, traditional PoW incurs extreme computational and energy waste over the blockchain. This waste is considered to be one of the biggest problems in PoW-based blockchains and cryptocurrencies. In this work, we propose a new useful PoW called Proof-of-Useful-Randomness (PoUR) that mitigates the energy waste by incorporating pre-computed (disclosable) randomness into the PoW. The key idea is to inject special randomness into puzzles via algebraic commitments that can be stored and later disclosed. Unlike the traditional wasteful PoWs, our approach enables pre-computed commitments to be utilized by a vast array of public-key cryptography methods that require offline-online processing (e.g., digital signature, key exchange, zero-knowledge protocol). Moreover, our PoW preserves the desirable properties of the traditional PoW and therefore does not require a substantial alteration in the underlying protocol. We showed the security of our PoW, and then fully implemented it to validate its significant energy-saving capabilities. 

Abstract We report a combined experimental and computational study of the optical properties of individual silicon telluride (Si 2 Te 3 ) nanoplates. The p-type semiconductor Si 2 Te 3 has a unique layered crystal structure with hexagonal closed-packed Te sublattices and Si–Si dimers occupying octahedral intercalation sites. The orientation of the silicon dimers leads to unique optical and electronic properties. Two-dimensional Si 2 Te 3 nanoplates with thicknesses of hundreds of nanometers and lateral sizes of tens of micrometers are synthesized by a chemical vapor deposition technique. At temperatures below 150 K, the Si 2 Te 3 nanoplates exhibit a direct band structure with a band gap energy of 2.394 eV at 7 K and an estimated free exciton binding energy of 150 meV. Polarized reflection measurements at different temperatures show anisotropy in the absorption coefficient due to an anisotropic orientation of the silicon dimers, which is in excellent agreement with theoretical calculations of the dielectric functions. Polarized Raman measurements of single Si 2 Te 3 nanoplates at different temperatures reveal various vibrational modes, which agree with density functional perturbation theory calculations. The unique structural and optical properties of nanostructured Si 2 Te 3 hold great potential applications in optoelectronics and chemical sensing. 

Bismuth oxide is an important bismuth compound having applications in electronics, photo-catalysis and medicine. At the nanoscale, bismuth oxide experiences a variety of new physico-chemical properties because of its increased surface to volume ratio leading to potentially new applications. In this manuscript, we report for the very first time the synthesis of bismuth oxide (Bi 2 O 3 ) nano-flakes by pulsed laser ablation in liquids without any external assistance (no acoustic, electric field, or magnetic field). The synthesis was performed by irradiating, pure bismuth needles immerged in de-ionized water, at very high fluence ∼160 J cm −2 in order to be highly selective and only promote the growth of two-dimensional structures. The x - and y -dimensions of the flakes were around 1 μm in size while their thickness was 47.0 ± 12.7 nm as confirmed by AFM analysis. The flakes were confirmed to be α- and γ-Bi 2 O 3 by SAED and Raman spectroscopy. By using this mixture of flakes, we demonstrated that the nanostructures can be used as antimicrobial agents, achieving a complete inhibition of Gram positive (MSRA) and Gram negative bacteria (MDR-EC) at low concentration, ∼50 ppm. 

Oblivious Random Access Machine (ORAM) allows a client to hide the access pattern and thus, offers a strong level of privacy for data outsourcing. An ideal ORAM scheme is expected to offer desirable properties such as low client bandwidth, low server computation overhead, and the ability to compute over encrypted data. S3ORAM (CCS’17) is an efficient active ORAM scheme, which takes advantage of secret sharing to provide ideal properties for data outsourcing such as low client bandwidth, low server computation and low delay. Despite its merits, S3ORAM only offers security in the semi-honest setting. In practice, an ORAM protocol is likely to operate in the presence of malicious adversaries who might deviate from the protocol to compromise the client privacy. In this paper, we propose MACAO, a new multi-server ORAM framework, which offers integrity, access pattern obliviousness against active adversaries, and the ability to perform secure computation over the accessed data. MACAO harnesses authenticated secret sharing techniques and tree-ORAM paradigm to achieve low client communication, efficient server computation, and low storage overhead at the same time. We fully implemented MACAO and conducted extensive experiments in real cloud platforms (Amazon EC2) to validate the performance of MACAO compared with the state-of-the-art. Our results indicate that MACAO can achieve comparable performance to S3ORAM while offering security against malicious adversaries. MACAO is a suitable candidate for integration into distributed file systems with encrypted computation capabilities towards enabling an oblivious functional data outsourcing infrastructure. 

Oblivious Random Access Machine (ORAM) allows a client to hide the access pattern when accessing sensitive data on a remote server. It is known that there exists a logarithmic communication lower bound on any passive ORAM construction, where the server only acts as the storage service. This overhead, however, was shown costly for some applications. Several active ORAM schemes with server computation have been proposed to overcome this limitation. However, they mostly rely on costly homomorphic encryptions, whose performance is worse than passive ORAM. In this article, we propose S³ORAM, a new multi-server ORAM framework, which featuresO(1) client bandwidth blowup and low client storage without relying on costly cryptographic primitives. Our key idea is to harness Shamir Secret Sharing and a multi-party multiplication protocol on applicable binary tree-ORAM paradigms. This strategy allows the client to instruct the server(s) to perform secure and efficient computation on his/her behalf with a low intervention thereby, achieving a constant client bandwidth blowup and low server computational overhead. Our framework can also work atop a generalk-ary tree ORAM structure (k≥ 2). We fully implemented our framework, and strictly evaluated its performance on a commodity cloud platform (Amazon EC2). Our comprehensive experiments confirmed the efficiency of S³ORAM framework, where it is approximately 10× faster than the most efficient passive ORAM (i.e., Path-ORAM) for a moderate network bandwidth while being three orders of magnitude faster than active ORAM withO(1) bandwidth blowup (i.e., Onion-ORAM). We have open-sourced the implementation of our framework for public testing and adaptation.