Currently, when a security analyst discovers a vulnerability in critical software system, they must navigate a fraught dilemma: immediately disclosing the vulnerability to the public could harm the system’s users; whereas disclosing the vulnerability only to the software’s vendor lets the vendor disregard or deprioritize the security risk, to the detriment of unwittingly-affected users. A compelling recent line of work aims to resolve this by using Zero Knowledge (ZK) protocols that let analysts prove that they know a vulnerability in a program, without revealing the details of the vulnerability or the inputs that exploit it. In principle, this could be achieved by generic ZK techniques. In practice, ZK vulnerability proofs to date have been restricted in scope and expressibility, due to challenges related to generating proof statements that model real-world software at scale and to directly formulating violated properties. This article presents Cheesecloth , a novel proof-statement compiler, which proves practical vulnerabilities in ZK by soundly-but-aggressively preprocessing programs on public inputs, selectively revealing information about executed control segments, and formalizing information leakage using a novel storage-labeling scheme. Cheesecloth ’s practicality is demonstrated by generating ZK proofs of well-known vulnerabilities in (previous versions of) critical software, including the Heartbleed information leakage in OpenSSL, a memory vulnerability in the FFmpeg multimedia encoding framework, a cryptographic implementation bug in the Secure Scuttlebutt decentralised social network, and a denial of service vulnerability in OpenSSL.

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’ work linearly in the group size, rendering it prohibitively costly for large groups.We thus devise new protocols where the servers’ cost grows very slowly with the group size, while recipients’ 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 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’ cost is ~$3.36 per million messages scanned, where each message may address up to 15 recipients.

Although power LEDs have been integrated in various devices that perform cryptographic operations for decades, the cryptanalysis risk they pose has not yet been investigated. In this paper, we present optical cryptanalysis, a new form of cryptanalytic side-channel attack, in which secret keys are extracted by using a photodiode to measure the light emitted by a device's power LED and analyzing subtle fluctuations in the light intensity during cryptographic operations. We analyze the optical leakage of power LEDs of various consumer devices and the factors that affect the optical SNR. We then demonstrate end-to-end optical cryptanalytic attacks against a range of consumer devices (smartphone, smartcard, and Raspberry Pi, along with their USB peripherals) and recover secret keys (RSA, ECDSA, SIKE) from prior and recent versions of popular cryptographic libraries (GnuPG, Libgcrypt, PQCrypto-SIDH) from a maximum distance of 25 meters.

Zero-knowledge (ZK) protocols enable one party to prove to others that it knows a fact without revealing any information about the evidence for such knowledge. There exist ZK protocols for all problems in NP, and recent works developed highly efficient protocols for proving knowledge of satisfying assignments to Boolean formulas, circuits and other NP formalisms. This work shows an efficient protocol for the converse: proving formula unsatisfiability in ZK (when the prover posses a non-ZK proof). An immediate practical application is efficiently proving safety of secret programs.

Existing models for non-interactive MPC cannot provide full privacy for inputs, because they inherently leak the residual function (i.e., the output of the function on the honest parties’ input together with all possible values of the adversarial inputs). For example, in any non-interactive sealed-bid auction, the last bidder can figure out what was the highest previous bid. We present a new MPC model which avoids this privacy leak. To achieve this, we utilize a blockchain in a novel way, incorporating smart contracts and arbitrary parties that can be incentivized to perform computation (“bounty hunters,” akin to miners). Security is maintained under a monetary assumption about the parties: an honest party can temporarily supply a recoverable collateral of value higher than the computational cost an adversary can expend. We thus construct non-interactive MPC protocols with strong security guarantees (full security, no residual leakage) in the short term. Over time, as the adversary can invest more and more computational resources, the security guarantee decays. Thus, our model, which we call Gage MPC, is suitable for secure computation with limited-time secrecy, such as auctions. A key ingredient in our protocols is a primitive we call “Gage Time Capsules” (GaTC): a time capsule that allows a party to commit to a value that others are able to reveal but only at a designated computational cost. A GaTC allows a party to commit to a value together with a monetary collateral. If the original party properly opens the GaTC, it can recover the collateral. Otherwise, the collateral is used to incentivize bounty hunters to open the GaTC. This primitive is used to ensure completion of Gage MPC protocols on the desired inputs. As a requisite tool (of independent interest), we present a generalization of garbled circuit that are more robust: they can tolerate exposure of extra input labels. This is in contrast to Yao’s garbled circuits, whose secrecy breaks down if even a single extra label is exposed. Finally, we present a proof-of-concept implementation of a special case of our construction, yielding an auction functionality over an Ethereum-like blockchain.

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.

Access control in the Internet of Things (IoT) often depends on a situation --- for example, "the user is at home'' --- that can only be tracked using multiple devices. In contrast to the (well-studied) smartphone frameworks, enforcement of situational constraints in the IoT poses new challenges because access control is fundamentally decentralized. It takes place in multiple independent frameworks, subjects are often external to the enforcement system, and situation tracking requires cross-framework interaction and permissioning. Existing IoT frameworks entangle access-control enforcement and situation tracking. This results in overprivileged, redundant, inconsistent, and inflexible implementations. We design and implement a new approach to IoT access control. Our key innovation is to introduce "environmental situation oracles'' (ESOs) as first-class objects in the IoT ecosystem. An ESO encapsulates the implementation of how a situation is sensed, inferred, or actuated. IoT access-control frameworks can use ESOs to enforce situational constraints, but ESOs and frameworks remain oblivious to each other's implementation details. A single ESO can be used by multiple access-control frameworks across the ecosystem. This reduces inefficiency, supports consistent enforcement of common policies, and --- because ESOs encapsulate sensitive device-access rights --- reduces overprivileging. ESOs can be deployed at any layer of the IoT software stack where access control is applied. We implemented prototype ESOs for the IoT resource layer, based on the IoTivity framework, and for the IoT Web services, based on the Passport middleware.

We propose a new notion of secure multiparty computation aided by a computationally powerful but untrusted “cloud” server. In this notion, on-the-fly multiparty computation (MPC), the cloud can noninteractively perform arbitrary dynamically chosen computations on data belonging to arbitrary dynamically chosen sets of users chosen. All users' input data and intermediate results are protected from snooping by the cloud as well as other users. This extends the standard notion of fully homomorphic encryption (FHE), where users can only enlist the cloud's help in evaluating functions on their own encrypted data. In on-the-fly MPC, each user is involved only when initially uploading his (encrypted) data to the cloud and in a final output decryption phase when outputs are revealed; the complexity of both is independent of the function being computed and the total number of users in the system. When users upload their data, they need not decide in advance which function will be computed, nor who they will compute with; they need only retroactively approve the eventually chosen functions and on whose data the functions were evaluated. This notion is qualitatively the best possible in minimizing interaction, since the users' interaction in the decryption stage is inevitable: we show that removing it would imply generic program obfuscation and is thus impossible. Our contributions are two-fold: (1) We define the new notion of multikey FHE, an enhanced FHE system which is capable of operating on inputs encrypted under multiple, unrelated keys. A ciphertext resulting from a multikey homomorphic evaluation can be jointly decrypted using the secret keys of all the users involved in the computation. We show how on-the-fly MPC can be achieved using any multikey FHE scheme. (2) We construct a multikey FHE scheme based on NTRU, a very efficient public-key encryption scheme proposed in the 1990s. It was previously not known how to make NTRU fully homomorphic even for a single party.

We show that elliptic-curve cryptography implementations on mobile devices are vulnerable to electromagnetic and power side-channel attacks. We demonstrate full extraction of ECDSA secret signing keys from OpenSSL and CoreBitcoin running on iOS devices, and partial key leakage from OpenSSL running on Android and from iOS's CommonCrypto. These non-intrusive attacks use a simple magnetic probe placed in proximity to the device, or a power probe on the phone's USB cable. They use a bandwidth of merely a few hundred kHz, and can be performed cheaply using an audio card and an improvised magnetic probe.

In mobile platforms and their app markets, controlling app permissions and preventing abuse of private information are crucial challenges. Information Flow Control (IFC) is a powerful approach for formalizing and answering user concerns such as: "Does this app send my geolocation to the Internet?" Yet despite intensive research efforts, IFC has not been widely adopted in mainstream programming practice. Abstract We observe that the typical structure of Android apps offers an opportunity for a novel and effective application of IFC. In Android, an app consists of a collection of a few dozen "components", each in charge of some high-level functionality. Most components do not require access to most resources. These components are a natural and effective granularity at which to apply IFC (as opposed to the typical process-level or language-level granularity). By assigning different permission labels to each component, and limiting information flow between components, it is possible to express and enforce IFC constraints. Yet nuances of the Android platform, such as its multitude of discretionary (and somewhat arcane) communication channels, raise challenges in defining and enforcing component boundaries.

Finite fields of characteristic 2 -- "binary fields" -- are used in a variety of applications in cryptography and data storage. Multiplication of two finite field elements is a fundamental operation and a well-known computational bottleneck in many of these applications, as they often require multiplication of a large number of elements. In this work we focus on accelerating multiplication in "large" binary fields of sizes greater than 232. We devise a new parallel algorithm optimized for execution on GPUs. This algorithm makes it possible to multiply large number of finite field elements, and achieves high performance via bit-slicing and fine-grained parallelization.

Since the invention of the camera, photos have been used to document reality and to supply proof of events. Yet today it is easy to fabricate realistic images depicting events that never happened. Thus, dozens of papers strive to develop methods for authenticating images. While some commercial cameras already attach digital signatures to photographs, the images often undergo subsequent transformations (cropping, rotation, compression, and so forth), which do not detract from their authenticity, but do change the image data and thus invalidate the signature. Existing methods address this by signing derived image properties that are invariant to some set of transformations. However, these are limited in the supported transformations, and often offer weak security guarantees. We present PhotoProof, a novel approach to image authentication based on cryptographic proofs. It can be configured, according to application requirements, to allow any permissible set of (efficiently computable) transformations. Starting with a signed image, our scheme attaches, to each legitimately derived image, a succinct proof of computational integrity attesting that the transformation was permissible. Anyone can verify these proofs, and generate updated proofs when applying further permissible transformations. Moreover, the proofs are zero-knowledge so that, for example, an authenticated cropped image reveals nothing about the cropped-out regions. PhotoProof is based on Proof-Carrying Data (PCD), a cryptographic primitive for secure execution of distributed computations. We describe the new construction, prove its security, and demonstrate a working prototype supporting a variety of permissible transformations.

Non-interactive zero-knowledge proofs (NIZKs) are a powerful cryptographic tool, with numerous potential applications. However, succinct NIZKs (e.g., zk-SNARK schemes) necessitate a trusted party to generate and publish some public parameters, to be used by all provers and verifiers. This party is trusted to correctly run a probabilistic algorithm (specified by the the proof system) that outputs the public parameters, and publish them, without leaking any other information (such as the internal randomness used by the algorithm), violating either requirement may allow malicious parties to produce convincing "proofs" of false statements. This trust requirement poses a serious impediment to deploying NIZKs in many applications, because a party that is trusted by all users of the envisioned system may simply not exist. In this work, we show how public parameters for a class of NIZKs can be generated by a multi-party protocol, such that if at least one of the parties is honest, then the result is secure (in both aforementioned senses) and can be subsequently used for generating and verifying numerous proofs without any further trust. We design and implement such a protocol, tailored to efficiently support the state-of-the-art NIZK constructions with short and easy-to-verify proofs (Parno et al. IEEE S&P '13, Ben-Sasson et al. USENIX Sec '14, Danezis et al., ASIACRYPT '14). Applications of our system include generating public parameters for systems such as Zero cash (Ben-Sasson et al. IEEE S&P '13) and the scalable zero-knowledge proof system of (Ben-Sasson et al. CRYPTO '14).

Edge networks connected to the Internet need effective monitoring techniques to inform routing decisions and detect violations of Service Level Agreements (SLAs). However, existing measurement tools, like ping, traceroute, and trajectory sampling, are vulnerable to attacks that can make a path look better than it really is. Here, we design and analyze a lightweight path-quality monitoring protocol that reliably raises an alarm when the packet-loss rate exceed a threshold, even when an adversary tries to bias monitoring results by selectively delaying, dropping, modifying, injecting, or preferentially treating packets. Our protocol is based on sublinear algorithms for sketching the second moment of stream of items and can monitor billions of packets using only 250-600 B of storage and the periodic transmission of a comparably sized IP packet. We also show how this protocol can be used to construct a more sophisticated protocol that allows the sender to localize the link responsible for the dropped packets. We prove that our protocols satisfy a precise definition of security, analyze their performance using numerical experiments, and derive analytic expressions for the tradeoff between statistical accuracy and system overhead. This paper contains a deeper treatment of results from earlier conference papers and several new results.

Bit coin is the first digital currency to see widespread adoption. While payments are conducted between pseudonyms, Bit coin cannot offer strong privacy guarantees: payment transactions are recorded in a public decentralized ledger, from which much information can be deduced. Zero coin (Miers et al., IEEE S&P 2013) tackles some of these privacy issues by unlinking transactions from the payment's origin. Yet, it still reveals payments' destinations and amounts, and is limited in functionality. In this paper, we construct a full-fledged ledger-based digital currency with strong privacy guarantees. Our results leverage recent advances in zero-knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs). First, we formulate and construct decentralized anonymous payment schemes (DAP schemes). A DAP scheme enables users to directly pay each other privately: the corresponding transaction hides the payment's origin, destination, and transferred amount. We provide formal definitions and proofs of the construction's security. Second, we build Zero cash, a practical instantiation of our DAP scheme construction. In Zero cash, transactions are less than 1 kB and take under 6 ms to verify - orders of magnitude more efficient than the less-anonymous Zero coin and competitive with plain Bit coin.

We study the question of protecting arithmetic circuits against additive attacks, which can add an arbitrary fixed value to each wire in the circuit. This extends the notion of algebraic manipulation detection (AMD) codes, which protect information against additive attacks, to that of AMD circuits which protect computation.

Physical computational devices leak side-channel information that may, and often does, reveal secret internal states. We present a general transformation that compiles any circuit into a circuit with the same functionality but resilience against well-defined classes of leakage. Our construction requires a small, stateless, and computation-independent leak-proof component that draws random elements from a fixed distribution. In essence, we reduce the problem of shielding arbitrarily complex circuits to the problem of shielding a single, simple component. Our approach is based on modeling the adversary as a powerful observer that inspects the device via a limited measurement apparatus. We allow the apparatus to access all the bits of the computation (except those inside the leak-proof component), and the amount of leaked information to grow unbounded over time. However, we assume that the apparatus is limited in the amount of output bits per iteration and the ability to decode certain linear encodings. While our results apply in general to such leakage classes, in particular, we obtain security against (a) constant-depth circuits leakage, where the leakage function is computed by an $\mathsf{AC}^0$ circuit (composed of NOT gates and unbounded fan-in AND and OR gates); (b) noisy leakage, where the leakage function reveals all the bits of the internal state of the circuit, but each bit is perturbed by independent binomial noise---i.e., flipped with some probability $p$. Namely, for some number $p\in(0,1/2]$, each bit of the computation is flipped with probability $p$, and remains unchanged with probability $1-p$.

Succinct arguments for NP are proof systems that allow a weak verifier to retroactively check computation done by a powerful prover. Constructions of such protocols prove membership in languages consisting of very large yet succinctly-represented constraint satisfaction problems that, alas, are unnatural in the sense that the problems that arise in practice are not in such form. For general computation tasks, the most natural representation is typically as random-access machine (RAM) algorithms, because such a representation can be obtained very efficiently by applying a compiler to code written in a high-level programming language. Thus, understanding the efficiency of reductions from RAM computations to other NP-complete problem representations for which succinct arguments (or proofs) are known is a prerequisite to a more complete understanding of the applicability of these arguments.

Probabilistically-Checkable Proofs (PCPs) form the algorithmic core that enables fast verification of long computations in many cryptographic constructions. Yet, despite the wonderful asymptotic savings they bring, PCPs are also the infamous computational bottleneck preventing these powerful cryptographic constructions from being used in practice. To address this problem, we present several results about the computational efficiency of PCPs. We construct the first PCP where the prover and verifier time complexities are quasi-optimal (i.e., optimal up to poly-logarithmic factors). The prover and verifier are also higly-parallelizable, and these computational guarantees hold even when proving and verifying the correctness of random-access machine computations. Our construction is explicit and has the requisite properties for being used in the cryptographic applications mentioned above.

Succinct non-interactive arguments of knowledge (SNARKs) enable verifying NP statements with complexity that is essentially independent of that required for classical NP verification. In particular, they provide strong solutions to the problem of verifiably delegating computation. We construct the first fully-succinct publicly-verifiable SNARK. To do that, we first show how to "bootstrap" any SNARK that requires expensive preprocessing to obtain a SNARK that does not, while preserving public verifiability. We then apply this transformation to known SNARKs with preprocessing. Moreover, the SNARK we construct only requires of the prover time and space that are essentially the same as that required for classical NP verification. Our transformation assumes only collision-resistant hashing; curiously, it does not rely on PCPs. We also show an analogous transformation for privately-verifiable SNARKs, assuming fully-homomorphic encryption.

The existence of succinct non-interactive arguments for NP (i.e., non-interactive computationally-sound proofs where the verifier's work is essentially independent of the complexity of the NP nondeterministic verifier) has been an intriguing question for the past two decades. Other than CS proofs in the random oracle model [Micali, FOCS '94], the only existing candidate construction is based on an elaborate assumption that is tailored to a specific protocol [Di Crescenzo and Lipmaa, CiE '08].

We propose a new notion of secure multiparty computation aided by a computationally-powerful but untrusted "cloud" server. In this notion that we call on-the-fly multiparty computation (MPC), the cloud can non-interactively perform arbitrary, dynamically chosen computations on data belonging to arbitrary sets of users chosen on-the-fly. All user's input data and intermediate results are protected from snooping by the cloud as well as other users. This extends the standard notion of fully homomorphic encryption (FHE), where users can only enlist the cloud's help in evaluating functions on their own encrypted data.

Third-party cloud computing represents the promise of outsourcing as applied to computation. Services, such as Microsoft's Azure and Amazon's EC2, allow users to instantiate virtual machines (VMs) on demand and thus purchase precisely the capacity they require when they require it. In turn, the use of virtualization allows third-party cloud providers to maximize the utilization of their sunk capital costs by multiplexing many customer VMs across a shared physical infrastructure. However, in this paper, we show that this approach can also introduce new vulnerabilities. Using the Amazon EC2 service as a case study, we show that it is possible to map the internal cloud infrastructure, identify where a particular target VM is likely to reside, and then instantiate new VMs until one is placed co-resident with the target. We explore how such placement can then be used to mount cross-VM side-channel attacks to extract information from a target VM on the same machine.

The Flume system is an implementation of decentralized information flow control (DIFC) at the operating system level. Prior work has shown Flume can be implemented as a practical extension tothe Linux operating system, allowing real Web applications to achieve useful security guarantees. However, the question remains if the Flume system is actually secure. This paper compares Flume with other recent DIFC systems like Asbestos, arguing that the latter is inherently susceptible to certain wide-bandwidth covert channels, and proving their absence in Flume by means of a noninterference proof in the Communicating Sequential Processes formalism.

Edge networks connected to the Internet need effective monitoring techniques to drive routing decisions and detect violations of Service Level Agreements (SLAs). However, existing measurement tools, like ping, traceroute, and trajectory sampling, are vulnerable to attacks that can make a path look better than it really is. In this paper, we design and analyze path-quality monitoring protocols that reliably raise an alarm when the packet-loss rate and delay exceed a threshold, even when an adversary tries to bias monitoring results by selectively delaying, dropping, modifying, injecting, or preferentially treating packets.