5 research outputs found
Robustness Guarantees for Mode Estimation with an Application to Bandits
Mode estimation is a classical problem in statistics with a wide range of
applications in machine learning. Despite this, there is little understanding
in its robustness properties under possibly adversarial data contamination. In
this paper, we give precise robustness guarantees as well as privacy guarantees
under simple randomization. We then introduce a theory for multi-armed bandits
where the values are the modes of the reward distributions instead of the mean.
We prove regret guarantees for the problems of top arm identification, top
m-arms identification, contextual modal bandits, and infinite continuous arms
top arm recovery. We show in simulations that our algorithms are robust to
perturbation of the arms by adversarial noise sequences, thus rendering modal
bandits an attractive choice in situations where the rewards may have outliers
or adversarial corruptions.Comment: 12 pages, 7 figures, 14 appendix page
Novel Approaches to Preserving Utility in Privacy Enhancing Technologies
Significant amount of individual information are being collected and analyzed today through a wide variety of applications across different industries. While pursuing better utility by discovering
knowledge from the data, an individual’s privacy may be compromised during an analysis: corporate networks monitor their online behavior, advertising companies collect and share their private
information, and cybercriminals cause financial damages through security breaches. To this end,
the data typically goes under certain anonymization techniques, e.g., CryptoPAn [Computer Networks’04], which replaces real IP addresses with prefix-preserving pseudonyms, or Differentially
Private (DP) [ICALP’06] techniques which modify the answer to a query by adding a zero-mean
noise distributed according to, e.g., a Laplace distribution. Unfortunately, most such techniques
either are vulnerable to adversaries with prior knowledge, e.g., some network flows in the data, or
require heavy data sanitization or perturbation, both of which may result in a significant loss of data
utility. Therefore, the fundamental trade-off between privacy and utility (i.e., analysis accuracy) has
attracted significant attention in various settings [ICALP’06, ACM CCS’14]. In line with this track
of research, in this dissertation we aim to build utility-maximized and privacy-preserving tools for
Internet communications. Such tools can be employed not only by dissidents and whistleblowers,
but also by ordinary Internet users on a daily basis. To this end, we combine the development of
practical systems with rigorous theoretical analysis, and incorporate techniques from various disciplines such as computer networking, cryptography, and statistical analysis. During the research,
we proposed three different frameworks in some well-known settings outlined in the following.
First, we propose The Multi-view Approach to preserve both privacy and utility in network trace
anonymization, Second, The R2DP Approach which is a novel technique on differentially private
mechanism design with maximized utility, and Third, The DPOD Approach that is a novel framework on privacy preserving Anomaly detection in the outsourcing setting