Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

/pdf/the-knowledge-complexity-of-interactive-proof-systems-31apre0ecf.pdf

The knowledge complexity of interactive proof-systems

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification. Standard denoiser suffers from the error amplification effect, in which small residual adversarial noise is progressively amplified and leads to wrong classifications. HGD overcomes this problem by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image. Compared with ensemble adversarial training which is the state-of-the-art defending method on large images, HGD has three advantages. First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks. Second, HGD can be trained on a small subset of the images and generalizes well to other images and unseen classes. Third, HGD can be transferred to defend models other than the one guiding it. In NIPS competition on defense against adversarial attacks, our HGD solution won the first place and outperformed other models by a large margin.1

/pdf/defense-against-adversarial-attacks-using-high-level-2ceio5fq0v.pdf

Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser

We design and implement a simple zero-knowledge argument protocol for NP whose communication complexity is proportional to the square-root of the verification circuit size. The protocol can be based on any collision-resistant hash function. Alternatively, it can be made non-interactive in the random oracle model, yielding concretely efficient zk-SNARKs that do not require a trusted setup or public-key cryptography. Our protocol is attractive not only for very large verification circuits but also for moderately large circuits that arise in applications. For instance, for verifying a SHA-256 preimage in zero-knowledge with 2-40 soundness error, the communication complexity is roughly 44KB (or less than 34KB under a plausible conjecture), the prover running time is 140 ms, and the verifier running time is 62 ms. This proof is roughly 4 times shorter than a similar proof of ZKB++ (Chase et al., CCS 2017), an optimized variant of ZKBoo (Giacomelli et al., USENIX 2016). The communication complexity of our protocol is independent of the circuit structure and depends only on the number of gates. For 2-40 soundness error, the communication becomes smaller than the circuit size for circuits containing roughly 3 million gates or more. Our efficiency advantages become even bigger in an amortized setting, where several instances need to be proven simultaneously. Our zero-knowledge protocol is obtained by applying an optimized version of the general transformation of Ishai et al. (STOC 2007) to a variant of the protocol for secure multiparty computation of Damgard and Ishai (Crypto 2006). It can be viewed as a simple zero-knowledge interactive PCP based on "interleaved" Reed-Solomon codes.

https://dl.acm.org/doi/pdf/10.1145/3133956.3134104

Ligero: Lightweight Sublinear Arguments Without a Trusted Setup

We design, implement, and evaluate a zero knowledge succinct non-interactive argument (SNARG) for Rank-1 Constraint Satisfaction (R1CS), a widely-deployed NP language undergoing standardization. Our SNARG has a transparent setup, is plausibly post-quantum secure, and uses lightweight cryptography. A proof attesting to the satisfiability of n constraints has size \(O(\log ^2 n)\); it can be produced with \(O(n \log n)\) field operations and verified with O(n). At 128 bits of security, proofs are less than \({250}\,\mathrm{kB}\) even for several million constraints, more than \(10{\times }\) shorter than prior SNARGs with similar features.

Aurora: Transparent Succinct Arguments for R1CS

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification. Standard denoiser suffers from the error amplification effect, in which small residual adversarial noise is progressively amplified and leads to wrong classifications. HGD overcomes this problem by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image. Compared with ensemble adversarial training which is the state-of-the-art defending method on large images, HGD has three advantages. First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks. Second, HGD can be trained on a small subset of the images and generalizes well to other images and unseen classes. Third, HGD can be transferred to defend models other than the one guiding it. In NIPS competition on defense against adversarial attacks, our HGD solution won the first place and outperformed other models by a large margin.

Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser

We consider a setting where users store their encrypted documents on a remote server and can selectively share documents with each other. A user should be able to perform keyword searches over all the documents she has access to, including the ones that others shared with her. The contents of the documents, and the search queries, should remain private from the server.

/pdf/multi-key-searchable-encryption-revisited-1kbhg4bgp7.pdf

Multi-Key Searchable Encryption, Revisited

Obfuscation, the task of compiling circuits or programs to make the internal computation unintelligible while preserving input/output functionality, has become an object of central focus in the cryptographic community. A work of Garg et al. [FOCS 2013] gave the first candidate obfuscator for general polynomial-size circuits, and led to several other works constructing candidate obfuscators. Each of these constructions is built upon another cryptographic primitive called a multilinear map, or alternatively a graded encoding scheme. Several of these candidates have been shown to achieve the strongest notion of security (virtual black-box, or VBB) against “purely algebraic” attacks in a model that we call the fully-restricted graded encoding model. In this model, each operation performed by an adversary is required to obey the algebraic restrictions of the graded encoding scheme. These restrictions essentially impose strong forms of homogeneity and multilinearity on the allowed polynomials. While important, the scope of the security proofs is limited by the stringency of these restrictions. We propose and analyze another variant of the Garg et al. obfuscator in a setting that imposes fewer restrictions on the adversary, which we call the arithmetic setting. This setting captures a broader class of attacks than considered in previous works. We also explore connections between notions of obfuscation security and longstanding questions in arithmetic circuit complexity. Our results include the following. � In the arithmetic setting where the adversary is limited to creating multilinear, but not necessarily homogenous polynomials, we obtain an unconditional proof of VBB security. This requires a substantially different analysis than previous security proofs. � In the arithmetic setting where the adversary can create polynomials of arbitrary degree, we show that a proof of VBB security for any currently available candidate obfuscator would imply VP 6 VNP. To complement this, we show that a weaker notion of security (indistinguishability obfuscation) can be achieved unconditionally in this setting, and that VBB security can be achieved under a complexity-theoretic assumption related to the ETH.

/pdf/protecting-obfuscation-against-arithmetic-attacks-3gqc40xkcg.pdf

Protecting obfuscation against arithmetic attacks

We consider a scenario where a server holds a huge database that it wants to make accessible to a large group of clients. After an initial setup phase, clients should be able to read arbitrary locations in the database while maintaining privacy (the server does not learn which locations are being read) and anonymity (the server does not learn which client is performing each read). This should hold even if the server colludes with a subset of the clients. Moreover, the run-time of both the server and the client during each read operation should be low, ideally only poly-logarithmic in the size of the database and the number of clients. We call this notion Private Anonymous Data Access (PANDA). PANDA simultaneously combines aspects of Private Information Retrieval (PIR) and Oblivious RAM (ORAM). PIR has no initial setup, and allows anybody to privately and anonymously access a public database, but the server’s run-time is linear in the data size. On the other hand, ORAM achieves poly-logarithmic server run-time, but requires an initial setup after which only a single client with a secret key can access the database. The goal of PANDA is to get the best of both worlds: allow many clients to privately and anonymously access the database as in PIR, while having an efficient server as in ORAM.

Private Anonymous Data Access

A probabilistically Checkable Proof (PCP) allows a randomized verifier, with oracle access to a purported proof, to probabilistically verify an input statement of the form “x ∈ L” by querying only few bits of the proof. A PCP of proximity (PCPP) has the additional feature of allowing the verifier to query only few bits of the input x, where if the input is accepted then the verifier is guaranteed that (with high probability) the input is close to some x′ ∈ L.

/pdf/probabilistically-checkable-proofs-of-proximity-with-zero-4twxzsyed1.pdf

Probabilistically Checkable Proofs of Proximity with Zero-Knowledge

Oblivious RAM (ORAM), introduced by Goldreich and Ostrovsky (JACM 1996), can be used to read and write to memory in a way that hides which locations are being accessed. The best known ORAM schemes have an \(O(\log n)\) overhead per access, where \(n\) is the data size. The work of Goldreich and Ostrovsky gave a lower bound showing that this is optimal for ORAM schemes that operate in a “balls and bins” model, where memory blocks can only be shuffled between different locations but not manipulated otherwise. The lower bound even extends to weaker settings such as offline ORAM, where all of the accesses to be performed need to be specified ahead of time, and read-only ORAM, which only allows reads but not writes. But can we get lower bounds for general ORAM, beyond “balls and bins”?

/pdf/is-there-an-oblivious-ram-lower-bound-for-online-reads-5dvsy50z8l.pdf

Is There an Oblivious RAM Lower Bound for Online Reads

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Mor Weiss

Author Tools

Chat about Author