Outsourcing Private RAM Computation

Garbled RAM, introduced by Lu and Ostrovsky, enables the task of garbling a RAM (Random Access Machine) program directly, there by avoiding the inefficient process of first converting it into a circuit. Garbled RAM can be seen as a RAM analogue of Yao's garbled circuit construction, except that known realizations of Garbled RAM make non-black-box use of the underlying cryptographic primitives. In this paper we remove this limitation and provide the first black-box construction of Garbled RAM with polylogarithmic overhead. Our scheme allows for garbling multiple RAM programs being executed on a persistent database and its security is based only on the existence of one-way functions. We also obtain the first secure RAM computation protocol that is both constant round and makes only black-box use of one-way functions in the Oblivious Transfer hybrid model.

We construct a general-purpose multi-input functional encryption scheme in the private-key setting. Namely, we construct a scheme where a functional key corresponding to a function f enables a user holding encryptions of $$x_1, \ldots , x_t$$x1,',xt to compute $$fx_1, \ldots , x_t$$fx1,',xt but nothing else. This is achieved starting from any general-purpose private-key single-input scheme without any additional assumptions, and is proven to be adaptively secure for any constant number of inputs t. Moreover, it can be extended to a super-constant number of inputs assuming that the underlying single-input scheme is sub-exponentially secure. Instantiating our construction with existing single-input schemes, we obtain multi-input schemes that are based on a variety of assumptions such as indistinguishability obfuscation, multilinear maps, learning with errors, and even one-way functions, offering various trade-offs between security and efficiency. Previous and concurrent constructions of multi-input functional encryption schemes either rely on stronger assumptions and provided weaker security guarantees Goldwasser et al. [EUROCRYPT '14], and Ananth and Jain [CRYPTO '15], or relied on multilinear maps and could be proven secure only in an idealized generic model Boneh et al. [EUROCRYPT '15]. In comparison, we present a general transformation that simultaneously relies on weaker assumptions and guarantees stronger security.

In this work, we study indistinguishability obfuscation and functional encryption for general circuits: Indistinguishability obfuscation requires that given any two equivalent circuits $C_0$ and $C_1$ of similar size, the obfuscations of $C_0$ and $C_1$ should be computationally indistinguishable. In functional encryption, ciphertexts encrypt inputs $x$ and keys are issued for circuits $C$. Using the key $\mathrm{SK}_C$ to decrypt a ciphertext $\mathrm{CT}_x={\sf Enc}(x)$ yields the value $C(x)$ but does not reveal anything else about $x$. Furthermore, no collusion of secret key holders should be able to learn anything more than the union of what they can each learn individually. We give constructions for indistinguishability obfuscation and functional encryption that supports all polynomial-size circuits. We accomplish this goal in three steps: (1) We describe a candidate construction for indistinguishability obfuscation for $\mathbf{NC}^1$ circuits. The security of this construction is based on a new algebraic hardness assumption. The candidate and assumption use a simplified variant of multilinear maps, which we call multilinear jigsaw puzzles. (2) We show how to use indistinguishability obfuscation for $\mathbf{NC}^1$ together with fully homomorphic encryption (with decryption in $\mathbf{NC}^1$) to achieve indistinguishability obfuscation for all circuits. (3) Finally, we show how to use indistinguishability obfuscation for circuits, public-key encryption, and noninteractive zero knowledge to achieve functional encryption for all circuits. The functional encryption scheme we construct also enjoys succinct ciphertexts, which enables several other applications.

Functional encryption (FE) enables fine-grained control of sensitive data by allowing users to only compute certain functions for which they have a key. The vast majority of work in FE has focused on deterministic functions, but for several applications such as privacy-aware auditing, differentially-private data release, proxy re-encryption, and more, the functionality of interest is more naturally captured by a randomized function. Recently, Goyal et al. (TCC 2015) initiated a formal study of FE for randomized functionalities with security against malicious encrypters, and gave a selectively secure construction from indistinguishability obfuscation. To date, this is the only construction of FE for randomized functionalities in the public-key setting. This stands in stark contrast to FE for deterministic functions which has been realized from a variety of assumptions.

Functional encryption (FE) can provide a fine-grained access control on the encrypted message. Therefore, it has been applied widely in security business. The previous works about functional encryptions most focused on the deterministic functions. The randomized algorithm has wide application, such as securely encryption algorithms against chosen ciphertext attack, privacy-aware auditing. Based on this, FE for randomized functions was proposed. The existing constructions are provided in a weaker selective security model, where the adversary is forced to output the challenge message before the start of experiment. This security is not enough in some scenes. In this work, we present a novel construction for FE, which supports the randomized functionalities. We use the technology of key encapsulated mechanism to achieve adaptive security under the simulated environment, where the adversary is allowed to adaptively choose the challenge message at any point in time. Our construction is built based on indistinguishability obfuscation, non-interactive witness indistinguishable proofs and perfectly binding commitment scheme.

We introduce and study the notion of non-interactive secure multiparty computation (NIMPC). An NIMPC protocol for a function f(x1,…,x n ) is specified by a joint probability distribution R = (R1,…,R n ) and local encoding functions Enc i (x i ,r i ), 1 ≤ i ≤ n. Given correlated randomness (r1,…,r n ) ∈ R R, each party P i , using its input x i and its randomness r i , computes the message m i = Enc i (x i ,r i ). The messages m1,…,m n can be used to decode f(x1,…,x n ). For a set T ⊆ [n], the protocol is said to be T-robust if revealing the messages \((\mathrm{Enc}_i(x_i,r_i))_{i\not\in T}\) together with the randomness (r i )i ∈ T gives the same information about \((x_i)_{i\not\in T}\) as an oracle access to the function f restricted to these input values. Namely, a coalition T can learn no more than the restriction of f fixing the inputs of uncorrupted parties, which, in this non-interactive setting, one cannot hope to hide. For 0 ≤ t ≤ n, the protocol is t-robust if it is T-robust for every T of size at most t and it is fully robust if it is n-robust. A 0-robust NIMPC protocol for f coincides with a protocol in the private simultaneous messages model of Feige et al. (STOC 1994).In the setting of computational (indistinguishability-based) security, fully robust NIMPC is implied by multi-input functional encryption, a notion that was recently introduced by Goldwasser et al. (Eurocrypt 2014) and realized using indistinguishability obfuscation. We consider NIMPC in the information-theoretic setting and obtain unconditional positive results for some special cases of interest: Group products. For every (possibly non-abelian) finite group G, the iterated group product function f(x1,…,x n ) = x1x2…x n admits an efficient, fully robust NIMPC protocol. Small functions. Every function f admits a fully robust NIMPC protocol whose complexity is polynomial in the size of the input domain (i.e., exponential in the total bit-length of the inputs). Symmetric functions. Every symmetric function f:X n → Y, where X is an input domain of constant size, admits a t-robust NIMPC protocol of complexity nO(t). For the case where f is a w-out-of-n threshold function, we get a fully robust protocol of complexity nO(w). On the negative side, we show that natural attempts to realize NIMPC using private simultaneous messages protocols and garbling schemes from the literature fail to achieve even 1-robustness.

The exact hardness of computing a Nash equilibrium is a fundamental open question in algorithmic game theory. This problem is complete for the complexity class PPAD. It is well known that problems in PPAD cannot be $$\mathrm {NP}$$ -complete unless $$\mathrm {NP}=\mathrm {coNP}$$ . Therefore, a natural direction is to reduce the hardness of PPAD to the hardness of problems used in cryptography. Bitansky, Paneth, and Rosen [FOCS 2015] prove the hardness of PPAD assuming the existence of quasi-polynomially hard indistinguishability obfuscation and sub-exponentially hard one-way functions. This leaves open the possibility of basing PPAD hardness on simpler, polynomially hard, computational assumptions. We make further progress in this direction and reduce PPAD hardness directly to polynomially hard assumptions. Our first result proves hardness of PPAD assuming the existence of polynomially hard indistinguishability obfuscation $$i\mathcal {O}$$ and one-way permutations. While this improves upon Bitansky et al.'s work, it does not give us a reduction to simpler, polynomially hard computational assumption because constructions of $$i\mathcal {O}$$ inherently seems to require assumptions with sub-exponential hardness. In contrast, public key functional encryption is a much simpler primitive and does not suffer from this drawback. Our second result shows that $$\mathsf{PPAD}$$ hardness can be based on polynomially hard compact public key functional encryption and one-way permutations. Our results further demonstrate the power of polynomially hard compact public key functional encryption which is believed to be weaker than indistinguishability obfuscation. Our techniques are general and we expect them to have various applications.

The general problem of finding minimal programs realizing given “program descriptions” is considered, where program descriptions may be of finite or infinite length and may specify arbitrary program properties. The problem of finding minimal programs consistent with finite or infinite input-output lists is a special case (for infinite input-output lists, this is a variant of E. M. Gold's function identification problem). Although most program minimization problems are not recursively solvable, they are found to be no more difficult than the problem of deciding whether any given program realizes any given description, or the problem of enumerating programs in order of nondecreasing length (whichever is harder). This result is formulated in terms of k -limiting recursive predicates and functionals, defined by repeated application of Gold's limit operator. A simple consequence is that the program minimization problem is limiting recursively solvable for finite input-output lists and 2-limiting recursively solvable for infinite input-output lists, with weak assumptions about the measure of program size. Gold regarded limiting function identification (more generally, “black box” identification) as a model of inductive thought. Intuitively, iterated limiting identification might be regarded as higher-order inductive inference performed collectively by an ever-growing community of lower order inductive inference machines.

In this paper, we propose a novel method for the detection of road accidents by analyzing audio streams for road surveillance application. In the last decade, due to the increase in a number of people and transportation vehicles, traffic accidents have become one of a major public issue worldwide. The vast number of injuries and death due to road traffic accident reveals the story of a global crisis of road safety. A total of 52,160 road traffic accidents (RTA), 1130 injuries and 85 fatalities were registered during the year 2000, in the state of Qatar. An increase in the number of transportation vehicles around cities has risen the need for more security and safety in public environments. The most obvious reason for a person's death during accidents is the absence or prolong response time of the first aid facility, which is due to the delay in the information of the accident being reached to the police, hospital or ambulance team. In the last couple of years, several surveillance systems have been proposed based on image and video processing for automatically detecting road accidents and car crashes to ensure a quick response by emergency teams. However, in some situations such as adverse weather conditions or cluttered environments, the visual information is not sufficient enough, whereas analyzing the audio tracks can significantly improve the overall reliability of surveillance systems. In this paper we propose a novel method that automatically identifies hazardous situations such as tire skidding and car crashes in presence of background noises, by analyzing the audio streams. Previous studies show that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency (TF) characteristics of such signals. The TF-based techniques have been proved to outperform classical techniques based on either time or frequency domains, in analyzing real-life nonstationary signals. Time-frequency distributions (TFDs) give additional information about signals that cannot be extracted from either the time domain or frequency domain representations, i.e. the instantaneous frequency of the components of a signal. In order to utilize this extra information provided by TF domain representation, the proposed approach extracts TF image features from quadratic time-frequency distributions (QTFDs), for the detection of audio anomalies. The extended modified-B distribution (EMBD) is utilized to transform a 1-dimensional audio signal into 2-dimensional TF representation which is interpreted as an image. The image descriptors based features are then extracted from the TF representation to classify visually the audio signals into background or abnormal activity patterns in the TF domain. The proposed features are based on Haralick's texture features extracted from the TF representation of audio signals considered and processed as a textured image. These features are used to characterize and hence classify audio signals into M classes. This research study demonstrates that a TF image pattern recognition approach offers significant advantages over standard signal classification methods that utilize either t-domain only or f-domain only features. The proposed method has been experimentally validated on a large open source database of sounds, including several kinds of background noise. The events to be recognized are superimposed on different background sounds of roads and traffic jam. The obtained results are compared with a recent study, utilizing the same large and complex data set of audio signals, and the same experimental setup. The overall classification results confirm the superior performance of the proposed approach with accuracy improvement up to 6%.

A well-known fact in the theory of secret sharing schemes is that shares must be of length at least as the secret itself. However, the proof of this lower bound uses the notion of information theoretic secrecy. A natural (and very practical) question is whether one can do better for secret sharing if the notion of secrecy is computational, namely, against resource bounded adversaries. In this note we observe that, indeed, one can do much better in the computational model (which is the one used in most applications).We present an m-threshold scheme, where m shares recover the secret but m - 1 shares give no (computational) information on the secret, in which shares corresponding to a secret S are of size |S|/m plus a short piece of information whose length does not depend on the secret size but just in the security parameter. (The bound of |S|/m is clearly optimal if the secret is to be recovered from m shares). Therefore, for moderately large secrets (a confidential file, a long message, a large data base) the savings in space and communication over traditional schemes is remarkable.The scheme is very simple and combines in a natural way traditional (perfect) secret sharing schemes, encryption, and information dispersal. It is provable secure given a secure (e.g., private key) encryption function.

A computer-assisted instruction (CAI) system was developed to teach electrical engineering problem solving at West Point, in an introductory course for nonengineers. The system, known as TUTOR301, takes the place of an individual tutor or coach in problem-solving methods. The simple form of solution text used minimized faculty time required to prepare solution scripts for the computer. The system's basic solution structure and program structure are described along with results gathered from five semesters of using this system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The structure of fuel element codes

A unifying theory of control dependence and its application to arbitrary program structures

Hippocampus segmentation in MR images is beneficial for the diagnosis of many diseases and pathologies such as Alzheimer’s disease. Manual segmentation of the hippocampus is highly time-consuming and has low reproducibility; however, automated methods have introduced substantial gains in this regard. In this study, we used a novel level-set method for hippocampus segmentation in combination with the SBGFRLS (Selective Binary and Gaussian Filtering Regularised Level Set) and LAC (Localising Region-Based Active Contours) algorithms. The proposed method avoided the algorithms which required a large database and instead used a more complex level set approach to obtain comparable accuracy. This method was applied to a set of 36 MRI scans provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI), using the Harmonised Hippocampal Protocol (HarP) as the gold standard. In addition, the results were compared with the outputs of the Freesurfer software package. In regards to the similarity indices, the results of our algorithm (mean Dice = 0.847) were more comparable with the gold standard compared to those of Freesurfer. Classification results for AD vs control and MCI vs control showed a high degree of accuracy (91% and 75%, respectively). Therefore, this method can be an option for accurate and robust segmentation of the hippocampus.

Practical reusable garbled circuits with parallel updates