A privacy preserving technique for distance-based classification with worst case privacy guarantees

There has been relatively little work on privacy preserving techniques for distance based mining. The most widely used ones are additive perturbation methods and orthogonal transform based methods. These methods concentrate on privacy protection in the average case and provide no worst case privacy guarantee. However, the lack of privacy guarantee makes it difficult to use these techniques in practice, and causes possible privacy breach under certain attacking methods. This paper proposes a novel privacy protection method for distance based mining algorithms that gives worst case privacy guarantees and protects the data against correlation-based and transform-based attacks. This method has the following three novel aspects. First, this method uses a framework to provide theoretical bound of privacy breach in the worst case. This framework provides easy to check conditions that one can determine whether a method provides worst case guarantee. A quick examination shows that special types of noise such as Laplace noise provide worst case guarantee, while most existing methods such as adding normal or uniform noise, as well as random projection method do not provide worst case guarantee. Second, the proposed method combines the favorable features of additive perturbation and orthogonal transform methods. It uses principal component analysis to decorrelate the data and thus guards against attacks based on data correlations. It then adds Laplace noise to guard against attacks that can recover the PCA transform. Third, the proposed method improves accuracy of one of the popular distance-based classification algorithms: K-nearest neighbor classification, by taking into account the degree of distance distortion introduced by sanitization. Extensive experiments demonstrate the effectiveness of the proposed method.     

Band gap reduction and redshift of lattice vibrational spectra in Nb and Fe co-doped PLZT

Nb and Fe co-doped PLZT (Pb_0.97La_0.02(Zr_0.52Ti_0.48)_1?2x (Nb_0.5Fe_0.5)_2x O₃ for x = 0.00, 0.02, 0.04, 0.06 and 0.08) samples have been prepared using sol–gel method. X-ray diffraction (XRD) and Raman spectroscopy studies confirmed that the samples are single phase even for the highest tested doping of 8 mol% of Nb and Fe. Incorporation of Nb and Fe atoms into PLZT lattice has been confirmed by XRD study where a systematic peak shift has been observed with increasing dopant concentration. The lattice parameters are found to decrease gradually with increase in Nb and Fe contents. From Raman spectroscopic investigation, redshift of several modes has been observed. Rietveld refinement has been performed to correlate XRD results with the fitting of Raman spectra. A total of 14 distinguished modes have been identified by de-convolution of Raman spectra, and they are in good agreement with the theoretically calculated modes for PbTiO₃ and also with those reported on PZT and PLZT previously. The Burstein–Moss shift of absorption edge has been observed by diffuse reflectance spectroscopy experiment, and the analysis shows change in band gap from 3.21 eV (for x = 0.00) to 2.59 eV (for x = 0.08). The underlying mechanisms and the observed electronic behavior have been confirmed and analyzed by photoluminescence study which revealed several transitions and supported the effect of Nb and Fe co-doping as observed from XRD and Raman spectroscopy.     

Athermal arrayed waveguide gratings in silicon-on-insulator by overlaying a polymer cladding on narrowed arrayed waveguides

Athermal arrayed waveguide gratings (AWGs) in silicon-on-insulator (SOI) are experimentally demonstrated for the first time to our knowledge. By using narrowed arrayed waveguides, and then overlaying a polymer layer, the wavelength temperature dependence of the AWGs is successfully reduced to -1.5 pm/°C, which is more than 1 order of magnitude less than that of normal SOI AWGs. The athermal behavior of the AWGs is obtained with little degradation of their performance. For the central channel, the cross talk is less than -15 dB and the insertion loss is around 2.6 dB. Good characteristics can be maintained with temperatures up to 75 °C. The total size of the device is 350 μm × 250 μm.     

A privacy-preserving technique for Euclidean distance-based mining algorithms using Fourier-related transforms

Privacy preserving data mining has become increasingly popular because it allows sharing of privacy-sensitive data for analysis purposes. However, existing techniques such as random perturbation do not fare well for simple yet widely used and efficient Euclidean distance-based mining algorithms. Although original data distributions can be pretty accurately reconstructed from the perturbed data, distances between individual data points are not preserved, leading to poor accuracy for the distance-based mining methods. Besides, they do not generally focus on data reduction. Other studies on secure multi-party computation often concentrate on techniques useful to very specific mining algorithms and scenarios such that they require modification of the mining algorithms and are often difficult to generalize to other mining algorithms or scenarios. This paper proposes a novel generalized approach using the well-known energy compaction power of Fourier-related transforms to hide sensitive data values and to approximately preserve Euclidean distances in centralized and distributed scenarios to a great degree of accuracy. Three algorithms to select the most important transform coefficients are presented, one for a centralized database case, the second one for a horizontally partitioned, and the third one for a vertically partitioned database case. Experimental results demonstrate the effectiveness of the proposed approach.