Synesthesia: Detecting Screen Content via Remote Acoustic Side Channels

We show that subtle acoustic noises emanating from within computer screens can be used to detect the content displayed on the screens. This sound can be picked up by ordinary microphones built into webcams or screens, and is inadvertently transmitted to other parties, e.g., during a videoconference call or archived recordings. It can also be recorded by a smartphone or "smart speaker" placed on a desk next to the screen, or from as far as 10 meters away using a parabolic microphone. Empirically demonstrating various attack scenarios, we show how this channel can be used for real-time detection of on-screen text, or users' input into on-screen virtual keyboards. We also demonstrate how an attacker can analyze the audio received during video call (e.g., on Google Hangout) to infer whether the other side is browsing the web in lieu of watching the video call, and which web site is displayed on their screen

Oblivious Message Retrieval

Anonymous message delivery systems, such as private messaging services and privacy-preserving payment systems, need a mechanism for recipients to retrieve the messages addressed to them, without leaking metadata or letting their messages be linked. Recipients could download all posted messages and scan for those addressed to them, but communication and computation costs are excessive at scale. We show how untrusted servers can detect messages on behalf of recipients, and summarize these into a compact encrypted digest that recipients can easily decrypt. These servers operate obliviously and do not learn anything about which messages are addressed to which recipients. Privacy, soundness, and completeness hold even if everyone but the recipient is adversarial and colluding (unlike in prior schemes). Our starting point is an asymptotically-efficient approach, using Fully Homomorphic Encryption and homomorphically-encoded Sparse Random Linear Codes. We then address the concrete performance using bespoke tailoring of lattice-based cryptographic components, alongside various algebraic and algorithmic optimizations. This reduces the digest size to a few bits per message scanned. Concretely, the servers\u27 cost is ~$1 per million messages scanned, and the resulting digests can be decoded by recipients in ~20ms. Our schemes can thus practically attain the strongest form of receiver privacy for current applications such as privacy-preserving cryptocurrencies

PerfOMR: Oblivious Message Retrieval with Reduced Communication and Computation

Anonymous message delivery, as in privacy-preserving blockchain and private messaging applications, needs to protect recipient metadata: eavesdroppers should not be able to link messages to their recipients. This raises the question: how can untrusted servers assist in delivering the pertinent messages to each recipient, without learning which messages are addressed to whom? Recent work constructed Oblivious Message Retrieval (OMR) protocols that outsource the message detection and retrieval in a privacy-preserving way, using homomorphic encryption. Their construction exhibits significant costs in computation per message scanned (${\sim}0.1$ second), as well as in the size of the associated messages (${\sim}1$kB overhead) and public keys (${\sim}132$kB). This work constructs more efficient OMR schemes, by replacing the LWE-based clue encryption of prior works with a Ring-LWE variant, and utilizing the resulting flexibility to improve several components of the scheme. We thus devise, analyze, and benchmark two protocols: The first protocol focuses on improving the detector runtime, using a new retrieval circuit that can be homomorphically evaluated $15$x faster than the prior work. The second protocol focuses on reducing the communication costs, by designing a different homomorphic decryption circuit that allows the parameter of the Ring-LWE encryption to be set such that the public key size is about $235$x smaller than the prior work, and the message size is roughly $1.6$x smaller. The runtime of this second construction is ${\sim}40.0$ms per message, still more than $2.5$x faster than prior works

Secure Association for the Internet of Things

Existing standards (ZigBee and Bluetooth Low Energy) for networked low-power wireless devices do not support secure association (or pairing) of new devices into a network: their association process is vulnerable to man-in-the-middle attacks. This paper addresses three essential aspects in attaining secure association for such devices. First, we define a user-interface primitive, oblivious comparison, that allows users to approve authentic associations and abort compromised ones. This distills and generalizes several existing approve/abort mechanisms, and moreover we experimentally show that OC can be implemented using very little hardware: one LED and one switch. Second, we provide a new Message Recognition Protocol (MRP) that allows devices associated using oblivious comparison to exchange authenticated messages without the use of public-key cryptography (which exceeds the capabilities of many IoT devices). This protocol improves upon previously proposed MRPs in several respects. Third, we propose a robust definition of security for MRPs that is based on universal composability, and show that our MRP satisfies this definition

Group Oblivious Message Retrieval

Anonymous message delivery, as in private communication and privacy-preserving blockchain applications, ought to protect recipient metadata: a message should not be inadvertently linkable to its destination. But how can messages then be delivered to each recipient, without each recipient scanning all messages? Recent work constructed Oblivious Message Retrieval (OMR) protocols that outsource this job to untrusted servers in a privacy-preserving manner. We consider the case of group messaging, where each message may have multiple recipients (e.g., in a group chat or blockchain transaction). Direct use of prior OMR protocols in the group setting increases the servers\u27 work linearly in the group size, rendering it prohibitively costly for large groups. We thus devise new protocols where the servers\u27 cost grows very slowly with the group size, while recipients\u27 cost is low and independent of the group size. Our approach uses Fully Homomorphic Encryption and other lattice-based techniques, building on and improving on prior work. The efficient handling of groups is attained by encoding multiple recipient-specific clues into a single polynomial or multilinear function that can be efficiently evaluated under FHE, and via preprocessing and amortization techniques. We formally study several variants of Group Oblivious Message Retrieval (GOMR) and describe corresponding GOMR protocols. Our implementation and benchmarks show, for parameters of interest, cost reductions of orders of magnitude compared to prior schemes. For example, the servers\u27 cost is ~3.36 per million messages scanned, where each message may address up to 15 recipients

Cluster Computing in Zero Knowledge

Large computations, when amenable to distributed parallel execution, are often executed on computer clusters, for scalability and cost reasons. Such computations are used in many applications, including, to name but a few, machine learning, webgraph mining, and statistical machine translation. Oftentimes, though, the input data is private and only the result of the computation can be published. Zero-knowledge proofs would allow, in such settings, to verify correctness of the output without leaking (additional) information about the input. In this work, we investigate theoretical and practical aspects of *zero-knowledge proofs for cluster computations*. We design, build, and evaluate zero-knowledge proof systems for which: (i) a proof attests to the correct execution of a cluster computation; and (ii) generating the proof is itself a cluster computation that is similar in structure and complexity to the original one. Concretely, we focus on MapReduce, an elegant and popular form of cluster computing. Previous zero-knowledge proof systems can in principle prove a MapReduce computation\u27s correctness, via a monolithic NP statement that reasons about all mappers, all reducers, and shuffling. However, it is not clear how to generate the proof for such monolithic statements via parallel execution by a distributed system. Our work demonstrates, by theory and implementation, that proof generation can be similar in structure and complexity to the original cluster computation. Our main technique is a bootstrapping theorem for succinct non-interactive arguments of knowledge (SNARKs) that shows how, via recursive proof composition and Proof-Carrying Data, it is possible to transform any SNARK into a *distributed SNARK for MapReduce* which proves, piecewise and in a distributed way, the correctness of every step in the original MapReduce computation as well as their global consistency

Get Your Hands Off My Laptop: Physical Side-Channel Key-Extraction Attacks on PCs

We demonstrate physical side-channel attacks on a popular software implementation of RSA and ElGamal, running on laptop computers. Our attacks use novel side channels, based on the observation that the ground electric potential, in many computers, fluctuates in a computation-dependent way. An attacker can measure this signal by touching exposed metal on the computer\u27s chassis with a plain wire, or even with a bare hand. The signal can also be measured at the remote end of Ethernet, VGA or USB cables. Through suitable cryptanalysis and signal processing, we have extracted 4096-bit RSA keys and 3072-bit ElGamal keys from laptops, via each of these channels, as well as via power analysis and electromagnetic probing. Despite the GHz-scale clock rate of the laptops and numerous noise sources, the full attacks require a few seconds of measurements using Medium Frequency signals (around 2 MHz), or one hour using Low Frequency signals (up to 40 kHz)

Cloud-Assisted Multiparty Computation from Fully Homomorphic Encryption

We construct protocols for secure multiparty computation with the help of a computationally powerful party, namely the cloud\u27\u27. Our protocols are simultaneously efficient in a number of metrics: * Rounds: our protocols run in 4 rounds in the semi-honest setting, and 5 rounds in the malicious setting. * Communication: the number of bits exchanged in an execution of the protocol is independent of the complexity of function f being computed, and depends only on the length of the inputs and outputs. * Computation: the computational complexity of all parties is independent of the complexity of the function f, whereas that of the cloud is linear in the size of the circuit computing f. In the semi-honest case, our protocol relies on the ``ring learning with errors\u27\u27 (RLWE) assumption, whereas in the malicious case, security is shown under the Ring LWE assumption as well as the existence of simulation-extractable NIZK proof systems and succinct non-interactive arguments. In the malicious setting, we also relax the communication and computation requirements above, and only require that they be ``small\u27\u27 -- polylogarithmic in the computation size and linear in the size of the joint size of the inputs. Our constructions leverage the key homomorphic property of the recent fully homomorphic encryption scheme of Brakerski and Vaikuntanathan (CRYPTO 2011, FOCS 2011). Namely, these schemes allow combining encryptions of messages under different keys to produce an encryption (of the sum of the messages) under the sum of the keys. We also design an efficient, non-interactive threshold decryption protocol for these fully homomorphic encryption schemes