Preserving Data-Privacy With Added Noises: Optimal Estimation and Privacy Analysis
نویسندگان
چکیده
منابع مشابه
Preserving Data-Privacy with Added Noises: Optimal Estimation and Privacy Analysis
Networked systems often relies on distributed algorithms to achieve a global computation goal with iterative local information exchanges between neighbor nodes. To preserve data privacy, a node may add a random noise to its original data for information exchange at each iteration. Nevertheless, a neighbor node can estimate other’s original data based on the information it received. The estimati...
متن کاملPrivacy-preserving Average Consensus: Privacy Analysis and Optimal Algorithm Design
The goal of the privacy-preserving average consensus (PPAC) is to guarantee the privacy of initial states and asymptotic consensus on the exact average of the initial value. This goal is achieved by an existing PPAC algorithm by adding and subtracting variance decaying and zero-sum random noises to the consensus process. However, there is lack of theoretical analysis to quantify the degree of t...
متن کاملPrivacy-Preserving Estimation
Data mining has evolved from a need to make sense of the enormous amounts of data generated by organizations. But data mining comes with its own cost, including possible threats to the confidentiality and privacy of individuals. This chapter presents a background on privacy-preserving data mining (PPDM) and the related field of statistical disclosure limitation (SDL). We then focus on privacy-p...
متن کاملPrivacy Preserving Data Mining
Recent interest in data collection and monitoring using data mining for security and business-related applications has raised privacy. Privacy Preserving Data Mining (PPDM) techniques require data modification to disinfect them from sensitive information or to anonymize them at an uncertainty level. This study uses PPDM with adult dataset to investigate effects of K-anonymization for evaluation...
متن کاملPrivacy Preserving Data Mining
There is a tremendous increase in the research of data mining. Data mining is the process of extraction of data from large database. Knowledge Discovery in database (KDD) is another name of data mining. Privacy protection has become a necessary requirement in many data mining applications due to emerging privacy legislation and regulations. One of the most important topics in research community...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Information Theory
سال: 2018
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2018.2842221