Semantically Secure Classifiers for Privacy Preserving Data Mining

Essential predictions are to be made by the parties distributed at multiple locations. However, in the process of building a model, perceptive data is not to be revealed. Maintaining the privacy of such data is a foremost concern. Earlier approaches developed for classification and prediction are proven not to be secure enough and the performance is affected. This chapter focuses on the secure construction of commonly used classifiers. The computations performed during model building are proved to be semantically secure. The homomorphism and probabilistic property of Paillier is used to perform secure product, mean, and variance calculations. The secure computations are performed without any intermediate data or the sensitive data at multiple sites being revealed. It is observed that the accuracy of the classifiers modeled is almost equivalent to the non-privacy preserving classifiers. Secure protocols require reduced computation time and communication cost. It is also proved that proposed privacy preserving classifiers perform significantly better than the base classifiers.

A Jackknife Model Averaging Analysis of RMB Misalignment Estimates

We adopt the Jackknife Model Averaging (JMA) technique to conduct a meta-regression analysis of 925 renminbi (RMB) misalignment estimates generated by 69 studies. The JMA method accounts for model selection and sampling uncertainties, and allows for non-nested model specifications and heteroskedasticity in assessing effects of study characteristics. The RMB misalignment estimates are found to be systematically affected by the choices of data, the theoretical setup and the empirical strategy, in addition to publication attributes of these studies. These study characteristic effects are quite robust to the choice of benchmark study characteristics, to alternative model averaging methods including the heteroskedasticity-robust Mallows approach, the information criterion approach, and the Bayesian model averaging. In evaluating the probabilistic property of RMB misalignment estimates implied by hypothetical composites of study characteristics, we find the evidence of a misaligned RMB, in general, is weak.

Accurate Classification Models for Distributed Mining of Privately Preserved Data

Data maintained at various sectors, needs to be mined to derive useful inferences. Larger part of the data is sensitive and not to be revealed while mining. Current methods perform privacy preservation classification either by randomizing, perturbing or anonymizing the data during mining. These forms of privacy preserving mining work well for data centralized at a single site. Moreover the amount of information hidden during mining is not sufficient. When perturbation approaches are used, data reconstruction is a major challenge. This paper aims at modeling classifiers for data distributed across various sites with respect to the same instances. The homomorphic and probabilistic property of Paillier is used to perform secure product, mean and variance calculations. The secure computations are performed without any intermediate data or the sensitive data at multiple sites being revealed. It is observed that the accuracy of the classifiers modeled is almost equivalent to the non-privacy preserving classifiers. Secure protocols require reduced computation time and communication cost.

Semantically Secure Classifiers for Privacy Preserving Data Mining

Essential predictions are to be made by the parties distributed at multiple locations. However, in the process of building a model, perceptive data is not to be revealed. Maintaining the privacy of such data is a foremost concern. Earlier approaches developed for classification and prediction are proven not to be secure enough and the performance is affected. This chapter focuses on the secure construction of commonly used classifiers. The computations performed during model building are proved to be semantically secure. The homomorphism and probabilistic property of Paillier is used to perform secure product, mean, and variance calculations. The secure computations are performed without any intermediate data or the sensitive data at multiple sites being revealed. It is observed that the accuracy of the classifiers modeled is almost equivalent to the non-privacy preserving classifiers. Secure protocols require reduced computation time and communication cost. It is also proved that proposed privacy preserving classifiers perform significantly better than the base classifiers.

Accurate Classification Models for Distributed Mining of Privately Preserved Data

Data maintained at various sectors, needs to be mined to derive useful inferences. Larger part of the data is sensitive and not to be revealed while mining. Current methods perform privacy preservation classification either by randomizing, perturbing or anonymizing the data during mining. These forms of privacy preserving mining work well for data centralized at a single site. Moreover the amount of information hidden during mining is not sufficient. When perturbation approaches are used, data reconstruction is a major challenge. This paper aims at modeling classifiers for data distributed across various sites with respect to the same instances. The homomorphic and probabilistic property of Paillier is used to perform secure product, mean and variance calculations. The secure computations are performed without any intermediate data or the sensitive data at multiple sites being revealed. It is observed that the accuracy of the classifiers modeled is almost equivalent to the non-privacy preserving classifiers. Secure protocols require reduced computation time and communication cost.

Probabilistic Patents

Economists often assume that a patent gives its owner a well-defined legalright to exclude others from practicing the invention described in thepatent. In practice, however, the rights afforded to patent holders arehighly uncertain. Under patent law, a patent is no guarantee of exclusionbut more precisely a legal right to try to exclude. Since only 0.1% of allpatents are litigated to trial, and since nearly half of fully litigatedpatents are declared invalid, this distinction is critical to understandingthe economic impact of patents. The growing recognition among economistsand legal scholars that patents are probabilistic property rights hassignificant implications for our understanding of patents in four importantareas: (1) reform of the system by which patents are granted; (2) the legaltreatment of patents in litigation; (3) the incentives of patent holdersand alleged infringers to settle their disputes through licensing orcross-licensing agreements rather than litigate them to completion; and (4)the antitrust limits on agreements between rivals that settle actual orthreatened patent litigation.

Identifying Culprits When Probabilistic Verification Fails

Automatic design verification techniques are intended to check that a particular system design meets a set of formal requirements. When the system does not meet the requirements, some verification tools can perform culprit identification to indicate which design components contributed to the failure. With non-probabilistic verification, culprit identification is straightforward: the verifier returns a counterexample trace that shows how the system can evolve to violate the desired property, and any component involved in that trace is a potential culprit. For probabilistic verification, the problem is more complicated, because no single trace constitutes a counterexample. Given a set of execution traces that collectively refute a probabilistic property, how should we interpret those traces to find which design components are primarily responsible? This paper discusses an approach to this problem based on decision-tree learning. Our solution provides rapid, scalable, and accurate diagnosis of culprits from execution traces. It rejects distractions and accurately focuses attention on the components that primarily cause a property verification to fail.