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In this paper, we propose a new secure machine learning inference platform assisted by a small dedicated security processor, which will be easier to protect and deploy compared to today's TEEs integrated into high-performance processors. Our platform provides three main advantages over the state-of-the-art: (i) We achieve significant performance improvements compared to state-of-the-art distributed Privacy-Preserving Machine Learning (PPML) protocols, with only a small security processor that is comparable to a discrete security chip such as the Trusted Platform Module (TPM) or on-chip security subsystems in SoCs similar to the Apple enclave processor. In the semi-honest setting with WAN/GPU, our scheme is 4X-63X faster than Falcon (PoPETs'21) and AriaNN (PoPETs'22) and 3.8X-12X more communication efficient. We achieve even higher performance improvements in the malicious setting. (ii) Our platform guarantees security with abort against malicious adversaries under honest majority assumption. (iii) Our technique is not limited by the size of secure memory in a TEE and can support high-capacity modern neural networks like ResNet18 and Transformer. While previous work investigated the use of high-performance TEEs in PPML, this work represents the first to show that even tiny secure hardware with very limited performance can be leveraged to significantly speed-up distributed PPML protocols if the protocol can be carefully designed for lightweight trusted hardware. 

Dynamic Searchable Symmetric Encryption (DSSE) provides efficient techniques for securely searching and updating an encrypted database. However, efficient DSSE schemes leak some sensitive information to the server. Recent works have implemented forward and backward privacy as security properties to reduce the amount of information leaked during update operations. Many attacks have shown that leakage from search operations can be abused to compromise the privacy of client queries. However, the attack literature has not rigorously investigated techniques to abuse update leakage. In this work, we investigate update leakage under DSSE schemes with forward and backward privacy from the perspective of a passive adversary. We propose two attacks based on a maximum likelihood estimation approach, the UFID Attack and the UF Attack, which target forward-private DSSE schemes with no backward privacy and Level 2 backward privacy, respectively. These are the first attacks to show that it is possible to leverage the frequency and contents of updates to recover client queries. We propose a variant of each attack which allows the update leakage to be combined with search pattern leakage to achieve higher accuracy. We evaluate our attacks against a real-world dataset and show that using update leakage can improve the accuracy of attacks against DSSE schemes, especially those without backward privacy. 

In recent years, there has been a heightened interest in the self-assembly of nanoparticles (NPs) that is mediated by their adsorption onto lipid membranes. The interplay between the adhesive energy of NPs on a lipid membrane and the membrane’s curvature energy causes it to wrap around the NPs. This results in an interesting membrane curvature-mediated interaction, which can lead to the self-assembly of NPs on lipid membranes. Recent studies have demonstrated that Janus spherical NPs, which adhere to lipid vesicles, can self-assemble into well-ordered nanoclusters with various geometries, including a few Platonic solids. The present study explores the additional effect of geometric anisotropy on the self-assembly of Janus NPs on lipid vesicles. Specifically, the current study utilized extensive molecular dynamics simulations to investigate the arrangement of Janus spherocylindrical NPs on lipid vesicles. We found that the additional geometric anisotropy significantly expands the range of NPs’ self-assemblies on lipid vesicles. The specific geometries of the resulting nanoclusters depend on several factors, including the number of Janus spherocylindrical NPs adhering to the vesicle and their aspect ratio. The lipid membrane-mediated self-assembly of NPs, demonstrated by this work, provides an alternative cost-effective route for fabricating highly engineered nanoclusters in three dimensions. Such structures, with the current wide range of material choices, have great potential for advanced applications, including biosensing, bioimaging, drug delivery, nanomechanics, and nanophotonics 

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. 

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.