Weak is Better: Tightly Secure Short Signatures from Weak PRFs

The Boyen-Li signature scheme [Asiacrypt\u2716] is a major theoretical breakthrough. Via a clever homomorphic evaluation of a pseudorandom function over their verification key, they achieve a reduction loss in security linear in the underlying security parameter and entirely independent of the number of message queries made, while still maintaining short signatures (consisting of a single short lattice vector). All previous schemes with such an independent reduction loss in security required a linear number of such lattice vectors, and even in the classical world, the only schemes achieving short signatures relied on non-standard assumptions. We improve on their result, providing a verification key smaller by a linear factor, a significantly tighter reduction with only a constant loss, and signing and verification algorithms that could plausibly run in about 1 second. Our main idea is to change the scheme in a manner that allows us to replace the pseudorandom function evaluation with an evaluation of a much more efficient weak pseudorandom function. As a matter of independent interest, we give an improved method of randomized inversion of the G gadget matrix [MP12], which reduces the noise growth rate in homomorphic evaluations performed in a large number of lattice-based cryptographic schemes, without incurring the high cost of sampling discrete Gaussians

Fully Collision-Resistant Chameleon-Hashes from Simpler and Post-Quantum Assumptions

Chameleon-hashes are collision-resistant hash-functions parametrized by a public key. If the corresponding secret key is known, arbitrary collisions for the hash can be found. Recently, Derler et al. (PKC \u2720) introduced the notion of fully collision-resistant chameleon-hashes. Full collision-resistance requires the intractability of finding collisions, even with full-adaptive access to a collision-finding oracle. Their construction combines simulation-sound extractable (SSE) NIZKs with perfectly correct IND-CPA secure public-key encryption (PKE) schemes. We show that, instead of perfectly correct PKE, non-interactive commitment schemes are sufficient. For the first time, this gives rise to efficient instantiations from plausible post-quantum assumptions and thus candidates of chameleon-hashes with strong collision-resistance guarantees and long-term security guarantees. On the more theoretical side, our results relax the requirement to not being dependent on public-key encryption

Elimination of deduplication and reduce communication overhead in cloud

We extend an attribute-based storage system with safe deduplication in a hybrid cloud setting, where a private cloud is accountable for duplicate detection and a public cloud manages the storage. Related with the prior data deduplication systems, our system has two compensations. It can be used to private portion data with users by agreeing access policies slightly distribution of decryption keys. It realizes the typical view of semantic security for data privacy while existing systems only accomplish it by critical and punier security notion. In adding, we set into view an organization to alter a cipher text over one starter policy into cipher texts of the equal plaintext but beneath other starter guidelines deprived of revealing the basic plaintext

Practical Witness Encryption for Algebraic Languages Or How to Encrypt Under Groth-Sahai Proofs

Witness encryption (WE) is a recent powerful encryption paradigm, which allows to encrypt a message using the description of a hard problem (a word in an NP-language) and someone who knows a solution to this problem (a witness) is able to efficiently decrypt the ciphertext. Recent work thereby focuses on constructing WE for NP complete languages (and thus NP). While this rich expressiveness allows flexibility w.r.t. applications, it makes existing instantiations impractical. Thus, it is interesting to study practical variants of WE schemes for subsets of NP that are still expressive enough for many cryptographic applications. We show that such WE schemes can be generically constructed from smooth projective hash functions (SPHFs). In terms of concrete instantiations of SPHFs (and thus WE), we target languages of statements proven in the popular Groth-Sahai (GS) non-interactive witness-indistinguishable/zero-knowledge proof framework. This allows us to provide a novel way to encrypt. In particular, encryption is with respect to a GS proof and efficient decryption can only be done by the respective prover. The so obtained constructions are entirely practical. To illustrate our techniques, we apply them in context of privacy-preserving exchange of information

Tightly Secure Chameleon Hash Functions in the Multi-User Setting and Their Applications

We define the security notion of (strong) collision resistance for chameleon hash functions in the multi-user setting ((S-)MU-CR security). We also present three constructions, CHF_dl, CHF_rsa and CHF_fac, and prove their tight S-MU-CR security based on the discrete logarithm, RSA and factoring assumptions, respectively. In applications, our tightly S-MU-CR secure chameleon hash functions help us to lift a signature scheme from (weak) unforgeability to strong unforgeability in the multi-user setting, and the security reduction is tightness preserving. Furthermore, they can also be used to construct tightly secure online/offline signatures, chameleon signatures and proxy signatures, etc., in the multi-user setting

A new deduplication and reduce communication overhead in cloud

We exhibited a novel way to deal with understand a property based capacity framework supporting secure deduplication. Our capacity framework is worked under a mixture cloud engineering, where a private cloud controls the calculation and an open cloud deals with the capacity. The private cloud is given a trapdoor key related with the comparing ciphertext, with which it can exchange the ciphertext more than one access strategy into ciphertexts of the equivalent plaintext under some other access approaches without monitoring the fundamental plaintext. Subsequent to accepting a capacity ask for, the private cloud first checks the legitimacy of the transferred thing through the appended evidence. On the off chance that the confirmation is legitimate, the private cloud runs a label coordinating calculation to see whether similar information hidden the ciphertext has been put away. Provided that this is true, at whatever point it is vital, it recovers the ciphertext into a ciphertext of the equivalent plaintext over an entrance approach which is the association set of both access strategies

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE

On the power of Public-key Function-Private Functional Encryption

In the public-key setting, known constructions of function-private functional encryption (FPFE) were limited to very restricted classes of functionalities like inner-product [Agrawal et al. - PKC 2015]. Moreover, its power has not been well investigated. In this paper, we construct FPFE for general functions and explore its powerful applications, both for general and specific functionalities. As warmup, we construct from FPFE a natural generalization of a signature scheme endowed with functional properties, that we call functional anonymous signature (FAS) scheme. In a FAS, Alice can sign a circuit C chosen from some distribution D to get a signature s and can publish a verification key that allows anybody holding a message m to verify that (1) s is a valid signature of Alice for some (possibly unknown to him) circuit C and (2) C(m)=1. Beyond unforgeability the security of FAS guarantees that the signature s hide as much information as possible about C except what can be inferred from knowledge of D. Then, we show that FPFE can be used to construct in a black-box way functional encryption schemes for randomized functionalities (RFE). %Previous constructions of (public-key) RFE relied on iO [Goyal et al. - TCC 2015]. As further application, we show that specific instantiations of FPFE can be used to achieve adaptively-secure CNF/DNF encryption for bounded degree formulae (BoolEnc). Though it was known how to implement BoolEnc from inner-product encryption (IPE) [Katz et al. - EUROCRYPT 2008], as already observed by Katz et al. this reduction only works for selective security and completely breaks down for adaptive security; however, we show that the reduction works if the IPE scheme is function-private. Finally, we present a general picture of the relations among all these related primitives. One key observation is that Attribute-based Encryption with function privacy implies FE, a notable fact that sheds light on the importance of the function privacy property for FE

cuFE: High Performance Privacy Preserving Support Vector Machine with Inner-Product Functional Encryption

Privacy preservation is a sensitive issue in our modern society. It is becoming increasingly important in many applications in this ever-growing and highly connected digital era. Functional encryption is a computation on encrypted data paradigm that allows users to retrieve the evaluation of a function on encrypted data without revealing the data, thus effectively protecting users\u27 privacy. However, existing functional encryption implementations are still very time-consuming for practical deployment, especially when applied to machine learning applications that involve a huge amount of data. In this paper, we present a high-performance implementation of inner-product functional encryption (IPFE) based on ring-learning with errors on graphics processing units. We propose novel techniques to parallelize the Gaussian sampling, which is one of the most time-consuming operations in the IPFE scheme. We further execute a systematic investigation to select the best strategy for implementing number theoretic transform and inverse number theoretic transform for different security levels. Compared to the existing AVX2 implementation of IPFE, our implementation on a RTX 2060 GPU device can achieve 34.24x, 40.02x, 156.30x, and 18.76x speed-up for Setup, Encrypt, KeyGen, and Decrypt respectively. Finally, we propose a fast privacy-preserving Support Vector Machine (SVM) application to classify data securely using our GPU-accelerated IPFE scheme. Experimental results show that our implementation can classify 100 inputs with 591 support vectors in 688 ms (less than a second), which is 33.12x faster than the AVX2 version which takes 23 seconds

Fully Collusion Resistant Trace-and-Revoke Functional Encryption for Arbitrary Identities

Functional Encryption (FE) has been extensively studied in the recent years, mainly focusing on the feasibility of constructing FE for general functionalities, as well as some realizations for restricted functionalities of practical interest, such as inner-product. However, little consideration has been given to the issue of key leakage on FE. The property of FE that allows multiple users to obtain the same functional keys from the holder of the master secret key raises an important problem: if some users leak their keys or collude to create a pirated decoder, how can we identify at least one of those users, given some information about the compromised keys or the pirated decoder? Moreover, how do we disable the decryption capabilities of those users (i.e. traitors)? Two recent works have offered potential solutions to the above traitor scenario. However, the two solutions satisfy weaker notions of security and traceability, can only tolerate bounded collusions (i.e., there is an a priori bound on the number of keys the pirated decoder obtains), or can only handle a polynomially large universe of possible identities. In this paper, we study trace-and-revoke mechanism on FE and provide the first construction of trace-and-revoke FE that supports arbitrary identities, is both fully collusion resistant and fully anonymous. Our construction relies on a generic transformation from revocable predicate functional encryption with broadcast (RPFE with broadcast, which is an extension of revocable predicate encryption with broadcast proposed by Kim and J. Wu at ASIACRYPT\u272020) to trace-and-revoke FE. Since this construction admits a generic construction of trace-and-revoke inner-product FE (IPFE), we instantiate the trace-and-revoke IPFE from the well-studied Learning with Errors (LWE). This is achieved by proposing a new LWE-based attribute-based IPFE (ABIPFE) scheme to instantiate RPFE with broadcast