Toward Fine-Grained Blackbox Separations Between Semantic and Circular-Security Notions

We address the problems of whether t-circular-secure encryption can be based on (t-1)-circular-secure encryption or on semantic (CPA) security, if t = 1. While for t = 1 a folklore construction, based on CPA-secure encryption, can be used to build a 1-circular-secure encryption with the same secret-key and message space, no such constructions are known for the bit-encryption case, which is of particular importance in fully-homomorphic encryption. Also, for $t \geq 2$, all constructions of t-circular-secure encryption (bitwise or otherwise) are based on specific assumptions. We make progress toward these problems by ruling out all fully-blackbox constructions of -- 1-seed circular-secure public-key bit encryption from CPA-secure public-key encryption; -- t-seed circular-secure public-key encryption from (t-1)-seed circular-secure public-key encryption, for any $t \geq 2$. Informally, seed-circular security is a variant of the circular security notion in which the seed of the key-generation algorithm, instead of the secret key, is encrypted. We also show how to extend our first result to rule out a large and non-trivial class of constructions of 1-circular-secure bit encryption, which we dub key-isolating constructions. Our separation model follows that of Gertner, Malkin and Reingold (FOCS’01), which is a weaker separation model than that of Impagliazzo and Rudich

Reproducible Circularly-Secure Bit Encryption: Applications and Realizations

We give generic constructions of several fundamental cryptographic primitives based on a new encryption primitive that combines circular security for bit encryption with the so-called reproducibility property (Bellare et al. PKC 2003). At the heart of our constructions is a novel technique which gives a way of de-randomizing reproducible public-key bit-encryption schemes and also a way of reducing one-wayness conditions of a constructed trapdoor-function family (TDF) to circular security of the base scheme. The main primitives that we build from our encryption primitive include k-wise one- way TDFs (Rosen and Segev TCC 2009), CCA2-secure encryption and deterministic encryption. Our results demonstrate a new set of applications of circularly-secure encryption beyond fully-homomorphic encryption and symbolic soundness. Finally, we show the plausibility of our assumptions by showing that the DDH-based circularly-secure scheme of Boneh et al. (Crypto 2008) and the subgroup indistinguishability based scheme of Brakerski and Goldwasser (Crypto 2010) are both reproducible

Linear-Time Zero-Knowledge Proofs for Arithmetic Circuit Satisfiability

We give computationally efficient zero-knowledge proofs of knowledge for arithmetic circuit satisfiability over a large field. For a circuit with N addition and multiplication gates, the prover only uses O(N)O(N) multiplications and the verifier only uses O(N)O(N) additions in the field. If the commitments we use are statistically binding, our zero-knowledge proofs have unconditional soundness, while if the commitments are statistically hiding we get computational soundness. Our zero-knowledge proofs also have sub-linear communication if the commitment scheme is compact. Our construction proceeds in three steps. First, we give a zero-knowledge proof for arithmetic circuit satisfiability in an ideal linear commitment model where the prover may commit to secret vectors of field elements, and the verifier can receive certified linear combinations of those vectors. Second, we show that the ideal linear commitment proof can be instantiated using error-correcting codes and non-interactive commitments. Finally, by choosing efficient instantiations of the primitives we obtain linear-time zero-knowledge proofs

Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging. © 2018 Behrouz Alizadeh Savareh et al., published by De Gruyter Open

Circular Security Is Complete for KDM Security

Circular security is the most elementary form of key-dependent message (KDM) security, which allows us to securely encrypt only a copy of secret key bits. In this work, we show that circular security is complete for KDM security in the sense that an encryption scheme satisfying this security notion can be transformed into one satisfying KDM security with respect to all functions computable by a-priori bounded-size circuits (bounded-KDM security). This result holds in the presence of any number of keys and in any of secret-key/public-key and CPA/CCA settings. Such a completeness result was previously shown by Applebaum (EUROCRYPT 2011) for KDM security with respect to projection functions (projection-KDM security) that allows us to securely encrypt both a copy and a negation of secret key bits. Besides amplifying the strength of KDM security, our transformation in fact can start from an encryption scheme satisfying circular security against CPA attacks and results in one satisfying bounded-KDM security against CCA attacks. This result improves the recent result by Kitagawa and Matsuda (TCC 2019) showing a CPA-to-CCA transformation for KDM secure public-key encryption schemes

Trapdoor Functions from the Computational Diffie-Hellman Assumption

Trapdoor functions (TDFs) are a fundamental primitive in cryptography. Yet, the current set of assumptions known to imply TDFs is surprisingly limited, when compared to public-key encryption. We present a new general approach for constructing TDFs. Specifically, we give a generic construction of TDFs from any Hash Encryption (Döttling and Garg [CRYPTO \u2717]) satisfying a novel property which we call recyclability. By showing how to adapt current Computational Diffie-Hellman (CDH) based constructions of hash encryption to yield recyclability, we obtain the first construction of TDFs with security proved under the CDH assumption. While TDFs from the Decisional Diffie-Hellman (DDH) assumption were previously known, the possibility of basing them on CDH had remained open for more than 30 years

Symbolic Encryption with Pseudorandom Keys

We give an efficient decision procedure that, on input two (acyclic) cryptographic expressions making arbitrary use of an encryption scheme and a (length doubling) pseudorandom generator, determines (in polynomial time) if the two expressions produce computationally indistinguishable distributions for any pseudorandom generator and encryption scheme satisfying the standard security notions of pseudorandomness and indistinguishability under chosen plaintext attack. The procedure works by mapping each expression to a symbolic pattern that captures, in a fully abstract way, the information revealed by the expression to a computationally bounded observer. We then prove that if any two (possibly cyclic) expressions are mapped to the same pattern, then the associated distributions are indistinguishable. At the same time, if the expressions are mapped to different symbolic patterns and do not contain encryption cycles, there are secure pseudorandom generators and encryption schemes for which the two distributions can be distinguished with overwhelming advantage

Minicrypt Primitives with Algebraic Structure and Applications

Algebraic structure lies at the heart of much of Cryptomania as we know it. An interesting question is the following: instead of building (Cryptomania) primitives from concrete assumptions, can we build them from simple Minicrypt primitives endowed with additional algebraic structure? In this work, we affirmatively answer this question by adding algebraic structure to the following Minicrypt primitives: • One-Way Function (OWF) • Weak Unpredictable Function (wUF) • Weak Pseudorandom Function (wPRF) The algebraic structure that we consider is group homomorphism over the input/output spaces of these primitives. We also consider a “bounded” notion of homomorphism where the primitive only supports an a priori bounded number of homomorphic operations in order to capture lattice-based and other “noisy” assumptions. We show that these structured primitives can be used to construct many cryptographic protocols. In particular, we prove that: • (Bounded) Homomorphic OWFs (HOWFs) imply collision-resistant hash functions, Schnorr-style signatures, and chameleon hash functions. • (Bounded) Input-Homomorphic weak UFs (IHwUFs) imply CPA-secure PKE, non-interactive key exchange, trapdoor functions, blind batch encryption (which implies anonymous IBE, KDM-secure and leakage-resilient PKE), CCA2 deterministic PKE, and hinting PRGs (which in turn imply transformation of CPA to CCA security for ABE/1-sided PE). • (Bounded) Input-Homomorphic weak PRFs (IHwPRFs) imply PIR, lossy trapdoor functions, OT and MPC (in the plain model). In addition, we show how to realize any CDH/DDH-based protocol with certain properties in a generic manner using IHwUFs/IHwPRFs, and how to instantiate such a protocol from many concrete assumptions. We also consider primitives with substantially richer structure, namely Ring IHwPRFs and L-composable IHwPRFs. In particular, we show the following: • Ring IHwPRFs with certain properties imply FHE. • 2-composable IHwPRFs imply (black-box) IBE, and $L$-composable IHwPRFs imply non-interactive $(L + 1)$-party key exchange. Our framework allows us to categorize many cryptographic protocols based on which structured Minicrypt primitive implies them. In addition, it potentially makes showing the existence of many cryptosystems from novel assumptions substantially easier in the future

ICAR: endoscopic skull‐base surgery

n/

Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities

The demand for renewable energy generation, especially photovoltaic (PV) power generation, has been growing over the past few years. However, the amount of generated energy by PV systems is highly dependent on weather conditions. Therefore, accurate forecasting of generated PV power is of importance for large-scale deployment of PV systems. Recently, machine learning (ML) methods have been widely used for PV power generation forecasting. A variety of these techniques, including artificial neural networks (ANNs), ridge regression, K-nearest neighbour (kNN) regression, decision trees, support vector regressions (SVRs) have been applied for this purpose and achieved good performance. In this paper, we briefly review the most recent ML techniques for PV energy generation forecasting and propose a new regression technique to automatically predict a PV system’s output based on historical input parameters. More specifically, the proposed loss function is a combination of three well-known loss functions: Correntropy, Absolute and Square Loss which encourages robustness and generalization jointly. We then integrate the proposed objective function into a Deep Learning model to predict a PV system’s output. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via back propagation. We investigate the effectiveness of the proposed method through comprehensive experiments on real data recorded by a real PV system. The experimental results confirm that our method outperforms the state-of-the-art ML methods for PV energy generation forecasting