Efficient fuzzy matching and intersection on private datasets

At Eurocrypt’04, Freedman, Nissim and Pinkas introduced a fuzzy private matching problem. The problem is defined as follows. Given two parties, each of them having a set of vectors where each vector has T integer components, the fuzzy private matching is to securely test if each vector of one set matches any vector of another set for at least t components where t < T. In the conclusion of their paper, they asked whether it was possible to design a fuzzy private matching protocol without incurring a communication complexity with the factor (T t ) . We answer their question in the affirmative by presenting a protocol based on homomorphic encryption, combined with the novel notion of a share-hiding error-correcting secret sharing scheme, which we show how to implement with efficient decoding using interleaved Reed-Solomon codes. This scheme may be of independent interest. Our protocol is provably secure against passive adversaries, and has better efficiency than previous protocols for certain parameter values.

Distributed private matching and set operations

Motivated by the need of private set operations in a distributed environment, we extend the two-party private matching problem proposed by Freedman, Nissim and Pinkas (FNP) at Eurocrypt’04 to the distributed setting. By using a secret sharing scheme, we provide a distributed solution of the FNP private matching called the distributed private matching. In our distributed private matching scheme, we use a polynomial to represent one party’s dataset as in FNP and then distribute the polynomial to multiple servers. We extend our solution to the distributed set intersection and the cardinality of the intersection, and further we show how to apply the distributed private matching in order to compute distributed subset relation. Our work extends the primitives of private matching and set intersection by Freedman et al. Our distributed construction might be of great value when the dataset is outsourced and its privacy is the main concern. In such cases, our distributed solutions keep the utility of those set operations while the dataset privacy is not compromised. Comparing with previous works, we achieve a more efficient solution in terms of computation. All protocols constructed in this paper are provably secure against a semi-honest adversary under the Decisional Diffie-Hellman assumption.

Unconditionally secure disjointness tests for private datasets

We present two unconditional secure protocols for private set disjointness tests. In order to provide intuition of our protocols, we give a naive example that applies Sylvester matrices. Unfortunately, this simple construction is insecure as it reveals information about the intersection cardinality. More specifically, it discloses its lower bound. By using the Lagrange interpolation, we provide a protocol for the honest-but-curious case without revealing any additional information. Finally, we describe a protocol that is secure against malicious adversaries. In this protocol, a verification test is applied to detect misbehaving participants. Both protocols require O(1) rounds of communication. Our protocols are more efficient than the previous protocols in terms of communication and computation overhead. Unlike previous protocols whose security relies on computational assumptions, our protocols provide information theoretic security. To our knowledge, our protocols are the first ones that have been designed without a generic secure function evaluation. More important, they are the most efficient protocols for private disjointness tests in the malicious adversary case.

Efficient disjointness tests for private datasets

We present efficient protocols for private set disjointness tests. We start from an intuition of our protocols that applies Sylvester matrices. Unfortunately, this simple construction is insecure as it reveals information about the cardinality of the intersection. More specifically, it discloses its lower bound. By using the Lagrange interpolation we provide a protocol for the honest-but-curious case without revealing any additional information. Finally, we describe a protocol that is secure against malicious adversaries. The protocol applies a verification test to detect misbehaving participants. Both protocols require O(1) rounds of communication. Our protocols are more efficient than the previous protocols in terms of communication and computation overhead. Unlike previous protocols whose security relies on computational assumptions, our protocols provide information theoretic security. To our knowledge, our protocols are first ones that have been designed without a generic secure function evaluation. More importantly, they are the most efficient protocols for private disjointness tests for the malicious adversary case.

Building Trust for Lambda-Congenial Secret Groups

Establishing trust while preserving privacy is a challenging research problem. In this paper we introduce lambda -congenial secret groups which allow users to recognize trusted partners based on common attributes while preserving their anonymity and privacy. Such protocols are different from authentication protocols, since the latter are based on identities, while the former are based on attributes. Introducing attributes in trust establishment allows a greater flexibility but also brings up several issues. In this paper, we investigate the problem of building trust with attributes by presenting motivating examples, analyzing the security requirements and giving an informal definition. We also survey one of the most related techniques, namely private matching, and finally present solutions based on it.

A study on matching and multi-objective fuzzy control strategy of heavy truck suspension system

Matching method of heavy truck-rear air suspensions is discussed, and a fuzzy control strategy which improves both ride comfort and road friendliness of truck by adjusting damping coefficients of the suspension system is found. In the first place, a Dongfeng EQ1141G7DJ heavy truck’s ten DOF whole vehicle-road model was set up based on Matlab/Simulink and vehicle dynamics. Then appropriate passive air suspensions were chosen to replace the original rear leaf springs of the truck according to truck-suspension matching criterions, consequently, the stiffness of front leaf springs were adjusted too. Then the semi-active fuzzy controllers were designed for further enhancement of the truck’s ride comfort and the road friendliness. After the application of semi-active fuzzy control strategy through simulation, is was indicated that both ride comfort and road friendliness could be enhanced effectively under various road conditions. The strategy proposed may provide theory basis for design and development of truck suspension system in China.

Finding and matching communities in social networks using data mining

The rapid growth in the number of users using social networks and the information that a social network requires about their users make the traditional matching systems insufficiently adept at matching users within social networks. This paper introduces the use of clustering to form communities of users and, then, uses these communities to generate matches. Forming communities within a social network helps to reduce the number of users that the matching system needs to consider, and helps to overcome other problems from which social networks suffer, such as the absence of user activities' information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased using the community information.

Development of distributed fuzzy systems with a runtime-adaptable mobile components framework

A distributed fuzzy system is a real-time fuzzy system in which the input, output and computation may be located on different networked computing nodes. The ability for a distributed software application, such as a distributed fuzzy system, to adapt to changes in the computing network at runtime can provide real-time performance improvement and fault-tolerance. This paper introduces an Adaptable Mobile Component Framework (AMCF) that provides a distributed dataflow-based platform with a fine-grained level of runtime reconfigurability. The execution location of small fragments (possibly as little as few machine-code instructions) of an AMCF application can be moved between different computing nodes at runtime. A case study is included that demonstrates the applicability of the AMCF to a distributed fuzzy system scenario involving multiple physical agents (such as autonomous robots). Using the AMCF, fuzzy systems can now be developed such that they can be distributed automatically across multiple computing nodes and are adaptable to runtime changes in the networked computing environment. This provides the opportunity to improve the performance of fuzzy systems deployed in scenarios where the computing environment is resource-constrained and volatile, such as multiple autonomous robots, smart environments and sensor networks.

Shortest path and vehicle trajectory aided map-matching for low frequency GPS data

Map-matching algorithms that utilise road segment connectivity along with other data (i.e.position, speed and heading) in the process of map-matching are normally suitable for high frequency (1 Hz or higher) positioning data from GPS. While applying such map-matching algorithms to low frequency data (such as data from a fleet of private cars, buses or light duty vehicles or smartphones), the performance of these algorithms reduces to in the region of 70% in terms of correct link identification, especially in urban and sub-urban road networks. This level of performance may be insufficient for some real-time Intelligent Transport System (ITS) applications and services such as estimating link travel time and speed from low frequency GPS data. Therefore, this paper develops a new weight-based shortest path and vehicle trajectory aided map-matching (stMM) algorithm that enhances the map-matching of low frequency positioning data on a road map. The well-known A* search algorithm is employed to derive the shortest path between two points while taking into account both link connectivity and turn restrictions at junctions. In the developed stMM algorithm, two additional weights related to the shortest path and vehicle trajectory are considered: one shortest path-based weight is related to the distance along the shortest path and the distance along the vehicle trajectory, while the other is associated with the heading difference of the vehicle trajectory. The developed stMM algorithm is tested using a series of real-world datasets of varying frequencies (i.e. 1 s, 5 s, 30 s, 60 s sampling intervals). A high-accuracy integrated navigation system (a high-grade inertial navigation system and a carrier-phase GPS receiver) is used to measure the accuracy of the developed algorithm. The results suggest that the algorithm identifies 98.9% of the links correctly for every 30 s GPS data. Omitting the information from the shortest path and vehicle trajectory, the accuracy of the algorithm reduces to about 73% in terms of correct link identification. The algorithm can process on average 50 positioning fixes per second making it suitable for real-time ITS applications and services.

An application of fuzzy DL-Based semantic perception to soil container classification

Semantic perception and object labeling are key requirements for robots interacting with objects on a higher level. Symbolic annotation of objects allows the usage of planning algorithms for object interaction, for instance in a typical fetchand-carry scenario. In current research, perception is usually based on 3D scene reconstruction and geometric model matching, where trained features are matched with a 3D sample point cloud. In this work we propose a semantic perception method which is based on spatio-semantic features. These features are defined in a natural, symbolic way, such as geometry and spatial relation. In contrast to point-based model matching methods, a spatial ontology is used where objects are rather described how they "look like", similar to how a human would described unknown objects to another person. A fuzzy based reasoning approach matches perceivable features with a spatial ontology of the objects. The approach provides a method which is able to deal with senor noise and occlusions. Another advantage is that no training phase is needed in order to learn object features. The use-case of the proposed method is the detection of soil sample containers in an outdoor environment which have to be collected by a mobile robot. The approach is verified using real world experiments.

Fuzzy ART

Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP synthesize fuzzy logic and ART networks by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic: intersection (∩) with the fuzzy intersection (∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric: theory in which the fuzzy inter:>ec:tion and the fuzzy union (∨), or component-wise maximum, play complementary roles. Complement coding preserves individual feature amplitudes while normalizing the input vector, and prevents a potential category proliferation problem. Adaptive weights :otart equal to one and can only decrease in time. A geometric interpretation of fuzzy AHT represents each category as a box that increases in size as weights decrease. A matching criterion controls search, determining how close an input and a learned representation must be for a category to accept the input as a new exemplar. A vigilance parameter (p) sets the matching criterion and determines how finely or coarsely an ART system will partition inputs. High vigilance creates fine categories, represented by small boxes. Learning stops when boxes cover the input space. With fast learning, fixed vigilance, and an arbitrary input set, learning stabilizes after just one presentation of each input. A fast-commit slow-recode option allows rapid learning of rare events yet buffers memories against recoding by noisy inputs. Fuzzy ARTMAP unites two fuzzy ART networks to solve supervised learning and prediction problems. A Minimax Learning Rule controls ARTMAP category structure, conjointly minimizing predictive error and maximizing code compression. Low vigilance maximizes compression but may therefore cause very different inputs to make the same prediction. When this coarse grouping strategy causes a predictive error, an internal match tracking control process increases vigilance just enough to correct the error. ARTMAP automatically constructs a minimal number of recognition categories, or "hidden units," to meet accuracy criteria. An ARTMAP voting strategy improves prediction by training the system several times using different orderings of the input set. Voting assigns confidence estimates to competing predictions given small, noisy, or incomplete training sets. ARPA benchmark simulations illustrate fuzzy ARTMAP dynamics. The chapter also compares fuzzy ARTMAP to Salzberg's Nested Generalized Exemplar (NGE) and to Simpson's Fuzzy Min-Max Classifier (FMMC); and concludes with a summary of ART and ARTMAP applications.

India’s disappearing common lands: fuzzy boundaries, encroachment, and evolving property rights

Opportunistic land encroachment occurs in many low-income countries, gradually yet pervasively, until discrete areas of common land disappear. This paper, motivated by field observations in Karnataka, India, demonstrates that such an evolution of property rights from common to private may be efficient when the boundaries between common and private land are poorly defined, or ‘‘fuzzy.’’ Using a multi-period optimization model, and introducing the concept of stock and flow enforcement, I show how effectiveness of enforcement effort, whether encroachment is reversible, and punitive fines, influence whether an area of common land is fully defined and protected or gradually or rapidly encroached.

Evaluation of cumulative externality: environmental economics, oligopoly and the private provision of public goods

This paper evaluates environmental externality when the structure of the externality is cumulative. The evaluation exercise is based on the assumption that the agents in question form conjectural variations. A number of environments are encompassed within this classification and have received due attention in the literature. Each of these heterogeneous environments, however, possesses considerable analytical homogeneity and permit subscription to a general model treatment. These environments include environmental externality, oligopoly and the analysis of the private provision of public goods. We highlight the general analytical approach by focusing on this latter context, in which debate centers around four issues: the existence of free-riding, the extent to which contributions are matched equally across individuals, the nature of conjectures consistent with equilibrium, and the allocative inefficiency of alternative regimes. This paper resolves each of these issues, with the following conclusions: A consistent-conjectures equilibrium exists in the private provision of public goods. It is the monopolistic-conjectures equilibrium. Agents act identically, contributing positive amounts of the public good in an efficient allocation of resources. There is complete matching of contributions among agents, no free-riding, and the allocation is independent of the number of members within the community. Thus the Olson conjecture—that inefficiency is exacerbated by community size—has no foundation in a consistent-conjectures, cumulative-externality, context (212 words).

A model of private bank-note issue

A random-matching model (ofmoney) is formulated in which there is complete public knowledge of the trading histories of a subset of the population, called the banking sector, and no public knowledge of the trading histories of the complement of that subset, called the non bank sector. Each person, whether a banker or a non banker, is assumed to have the technological capability to create indivisible and durable objects called notes. If outside money is indivisible and sufficiently scarce, then the optimal mechanism is shown to have note issue and note destruction (redemption) by members of the banking sector.

Design of a simplified neuro-fuzzy system based on CMAC Architectures

This work presents the design of a fuzzy controller with simplified architecture that use an artificial neural network working as the aggregation operator for several active fuzzy rules. The simplified architecture of the fuzzy controller is used to minimize the time processing used in the closed loop system operation, the basic procedures of fuzzification are simplified to maximum while all the inference procedures are computed in a private way. As consequence, this simplified architecture allows a fast and easy configuration of the simplified fuzzy controller. The structuring of the fuzzy rules that define the control actions is previously computed using an artificial neural network based on CMAC Cerebellar Model Articulation Controller. The operational limits are standardized and all the control actions are previously calculated and stored in memory. For applications, results and conclusions several configurations of this fuzzy controller are considered.