A Framework for Efficient Adaptively Secure Composable Oblivious Transfer in the ROM

Oblivious Transfer (OT) is a fundamental cryptographic protocol that finds a number of applications, in particular, as an essential building block for two-party and multi-party computation. We construct a round-optimal (2 rounds) universally composable (UC) protocol for oblivious transfer secure against active adaptive adversaries from any OW-CPA secure public-key encryption scheme with certain properties in the random oracle model (ROM). In terms of computation, our protocol only requires the generation of a public/secret-key pair, two encryption operations and one decryption operation, apart from a few calls to the random oracle. In~terms of communication, our protocol only requires the transfer of one public-key, two ciphertexts, and three binary strings of roughly the same size as the message. Next, we show how to instantiate our construction under the low noise LPN, McEliece, QC-MDPC, LWE, and CDH assumptions. Our instantiations based on the low noise LPN, McEliece, and QC-MDPC assumptions are the first UC-secure OT protocols based on coding assumptions to achieve: 1) adaptive security, 2) optimal round complexity, 3) low communication and computational complexities. Previous results in this setting only achieved static security and used costly cut-and-choose techniques.Our instantiation based on CDH achieves adaptive security at the small cost of communicating only two more group elements as compared to the gap-DH based Simplest OT protocol of Chou and Orlandi (Latincrypt 15), which only achieves static security in the ROM

Quantum Cryptography Beyond Quantum Key Distribution

Quantum cryptography is the art and science of exploiting quantum mechanical effects in order to perform cryptographic tasks. While the most well-known example of this discipline is quantum key distribution (QKD), there exist many other applications such as quantum money, randomness generation, secure two- and multi-party computation and delegated quantum computation. Quantum cryptography also studies the limitations and challenges resulting from quantum adversaries---including the impossibility of quantum bit commitment, the difficulty of quantum rewinding and the definition of quantum security models for classical primitives. In this review article, aimed primarily at cryptographers unfamiliar with the quantum world, we survey the area of theoretical quantum cryptography, with an emphasis on the constructions and limitations beyond the realm of QKD.Comment: 45 pages, over 245 reference

CryptoMaze: Atomic Off-Chain Payments in Payment Channel Network

Payment protocols developed to realize off-chain transactions in Payment channel network (PCN) assumes the underlying routing algorithm transfers the payment via a single path. However, a path may not have sufficient capacity to route a transaction. It is inevitable to split the payment across multiple paths. If we run independent instances of the protocol on each path, the execution may fail in some of the paths, leading to partial transfer of funds. A payer has to reattempt the entire process for the residual amount. We propose a secure and privacy-preserving payment protocol, CryptoMaze. Instead of independent paths, the funds are transferred from sender to receiver across several payment channels responsible for routing, in a breadth-first fashion. Payments are resolved faster at reduced setup cost, compared to existing state-of-the-art. Correlation among the partial payments is captured, guaranteeing atomicity. Further, two party ECDSA signature is used for establishing scriptless locks among parties involved in the payment. It reduces space overhead by leveraging on core Bitcoin scripts. We provide a formal model in the Universal Composability framework and state the privacy goals achieved by CryptoMaze. We compare the performance of our protocol with the existing single path based payment protocol, Multi-hop HTLC, applied iteratively on one path at a time on several instances. It is observed that CryptoMaze requires less communication overhead and low execution time, demonstrating efficiency and scalability.Comment: 30 pages, 9 figures, 1 tabl

Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction

Universally Composable Quantum Multi-Party Computation

The Universal Composability model (UC) by Canetti (FOCS 2001) allows for secure composition of arbitrary protocols. We present a quantum version of the UC model which enjoys the same compositionality guarantees. We prove that in this model statistically secure oblivious transfer protocols can be constructed from commitments. Furthermore, we show that every statistically classically UC secure protocol is also statistically quantum UC secure. Such implications are not known for other quantum security definitions. As a corollary, we get that quantum UC secure protocols for general multi-party computation can be constructed from commitments

Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

Callisto: a cryptographic approach to detecting serial perpetrators of sexual misconduct

Sexual misconduct is prevalent in workplace and education settings but stigma and risk of further damage deter many victims from seeking justice. Callisto, a non-profit that has created an online sexual assault reporting platform for college campuses, is expanding its work to combat sexual assault and harassment in other industries. In this new product, users will be invited to an online "matching escrow" that will detect repeat perpetrators and create pathways to support for victims. Users submit encrypted data about their perpetrator, and this data can only be decrypted by the Callisto Options Counselor (a lawyer), when another user enters the identity of the same perpetrator. If the perpetrator identities match, both users will be put in touch independently with the Options Counselor, who will connect them to each other (if appropriate) and help them determine their best path towards justice. The client relationships with the Options Counselors are structured so that any client-counselor communications would be privileged. A combination of client-side encryption, encrypted communication channels, oblivious pseudo-random functions, key federation, and Shamir Secret Sharing keep data confidential in transit, at rest, and during the matching process with the guarantee that only the lawyer ever has access to user submitted data, and even then only when a match is identified.Accepted manuscrip