By Lei Chen, Yan Jia, Timos Sellis, Guanfeng Liu
This publication constitutes the refereed lawsuits of the sixteenth Asia-Pacific convention APWeb 2014 held in Changsha, China, in September 2014. The 34 complete papers and 23 brief papers awarded have been conscientiously reviewed and chosen from 134 submissions. The papers handle learn, improvement and complex functions of large-scale facts administration, internet and seek applied sciences, and data processing.
Read or Download Web Technologies and Applications: 16th Asia-Pacific Web Conference, APWeb 2014, Changsha, China, September 5-7, 2014. Proceedings PDF
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Extra info for Web Technologies and Applications: 16th Asia-Pacific Web Conference, APWeb 2014, Changsha, China, September 5-7, 2014. Proceedings
Meanwhile, PrivBayes employs Bayesian networks to publish high-dimensional datasets without incurring prohibitive computation overheads or excessive noise injection. References 1. : Signal processing and machine learning with differential privacy: theory, algorithms, and challenges (September 2013) 2. : anonymized social networks, hidden patterns, and structural steganography, pp. 181–190 (2007) 3. : A face is exposed for AOL searcher no. 4417749. New York Times, August 9 (2006) 4. : “You might also like:” privacy risks of collaborative filtering.
Second, Privelet adds an independent Laplace noise to each wavelet coefficient in a way that ensures -differential privacy. This results in a new matrix C ∗ with noisy coefficients. Finally, Privelet maps C ∗ back to a noisy frequency matrix M ∗ , which is returned as the output. In the following, we clarify the details of Privelet. , M is a one-dimensional matrix), and introduce the onedimensional Haar wavelet transform (HWT). After that, we explain how this wavelet transform can be incorporated in Privelet.
In particular, it requires converting the input table T into a frequency matrix M whose number of entries is exponential to the number d of attributes in T – this incurs prohibitive overheads even when d is moderate. In addition, its noise variance bound (for range-count query results) is O((log m)d /ε2 ), which also increases exponentially with d. In fact, these deficiencies are not unique to Privelet: most existing techniques for differentially private data publishing require materializing M , and they provide poor data utility when d is large.