arXiv subjects

Michael Carl Tschantz

Publications and source records attributed to Michael Carl Tschantz.

18 recordsLinked to original sources

arXiv2023

Using Participants' Utility Functions to Compare Versions of Differential Privacy

We use decision theory to compare variants of differential privacy from the perspective of prospective study participants. We posit the existence of a preference ordering on the set of potential consequences that study participants can incur, which enables the analysis of individual utility functions. Drawing upon the theory of measurement, we argue that changes in expected utilities should be measured via the classic Euclidean metric. We then consider the question of which privacy guarantees would be more appealing for individuals under different decision settings. Through our analysis, we found that the nature of the potential participant's utility function, along with the specific values of $ε$ and $δ$, can greatly alter which privacy guarantees are preferable.

cs.CR↗

arXiv2022

What is Proxy Discrimination?

The near universal condemnation of proxy discrimination hides a disagreement over what it is. This work surveys various notions of proxy and proxy discrimination found in prior work and represents them in a common framework. These notions variously turn on statistical dependencies, causal effects, and intentions. It discusses the limitations and uses of each notation and of the concept as a whole.

cs.LG↗

arXiv2021

Avoiding Disparity Amplification under Different Worldviews

We mathematically compare four competing definitions of group-level nondiscrimination: demographic parity, equalized odds, predictive parity, and calibration. Using the theoretical framework of Friedler et al., we study the properties of each definition under various worldviews, which are assumptions about how, if at all, the observed data is biased. We argue that different worldviews call for different definitions of fairness, and we specify the worldviews that, when combined with the desire to avoid a criterion for discrimination that we call disparity amplification, motivate demographic parity and equalized odds. We also argue that predictive parity and calibration are insufficient for avoiding disparity amplification because predictive parity allows an arbitrarily large inter-group disparity and calibration is not robust to post-processing. Finally, we define a worldview that is more realistic than the previously considered ones, and we introduce a new notion of fairness that corresponds to this worldview.

cs.LG↗

arXiv2020

Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics

Bias in machine learning has manifested injustice in several areas, such as medicine, hiring, and criminal justice. In response, computer scientists have developed myriad definitions of fairness to correct this bias in fielded algorithms. While some definitions are based on established legal and ethical norms, others are largely mathematical. It is unclear whether the general public agrees with these fairness definitions, and perhaps more importantly, whether they understand these definitions. We take initial steps toward bridging this gap between ML researchers and the public, by addressing the question: does a lay audience understand a basic definition of ML fairness? We develop a metric to measure comprehension of three such definitions--demographic parity, equal opportunity, and equalized odds. We evaluate this metric using an online survey, and investigate the relationship between comprehension and sentiment, demographics, and the definition itself.

cs.CY↗

arXiv2019

Differential Privacy as a Causal Property

We present associative and causal views of differential privacy. Under the associative view, the possibility of dependencies between data points precludes a simple statement of differential privacy's guarantee as conditioning upon a single changed data point. However, we show that a simple characterization of differential privacy as limiting the effect of a single data point does exist under the causal view, without independence assumptions about data points. We believe this characterization resolves disagreement and confusion in prior work about the consequences of differential privacy. The associative view needing assumptions boils down to the contrapositive of the maxim that correlation doesn't imply causation: differential privacy ensuring a lack of (strong) causation does not imply a lack of (strong) association. Our characterization also opens up the possibility of applying results from statistics, experimental design, and science about causation while studying differential privacy.

cs.CR↗

arXiv2019

Assessing Post Deletion in Sina Weibo: Multi-modal Classification of Hot Topics

Widespread Chinese social media applications such as Weibo are widely known for monitoring and deleting posts to conform to Chinese government requirements. In this paper, we focus on analyzing a dataset of censored and uncensored posts in Weibo. Despite previous work that only considers text content of posts, we take a multi-modal approach that takes into account both text and image content. We categorize this dataset into 14 categories that have the potential to be censored on Weibo, and seek to quantify censorship by topic. Specifically, we investigate how different factors interact to affect censorship. We also investigate how consistently and how quickly different topics are censored. To this end, we have assembled an image dataset with 18,966 images, as well as a text dataset with 994 posts from 14 categories. We then utilized deep learning, CNN localization, and NLP techniques to analyze the target dataset and extract categories, for further analysis to better understand censorship mechanisms in Weibo. We found that sentiment is the only indicator of censorship that is consistent across the variety of topics we identified. Our finding matches with recently leaked logs from Sina Weibo. We also discovered that most categories like those related to anti-government actions (e.g. protest) or categories related to politicians (e.g. Xi Jinping) are often censored, whereas some categories such as crisis-related categories (e.g. rainstorm) are less frequently censored. We also found that censored posts across all categories are deleted in three hours on average.

cs.SI↗

arXiv2018

The Effectiveness of Privacy Enhancing Technologies against Fingerprinting

We measure how effective Privacy Enhancing Technologies (PETs) are at protecting users from website fingerprinting. Our measurements use both experimental and observational methods. Experimental methods allow control, precision, and use on new PETs that currently lack a user base. Observational methods enable scale and drawing from the browsers currently in real-world use. By applying experimentally created models of a PET's behavior to an observational data set, our novel hybrid method offers the best of both worlds. We find the Tor Browser Bundle to be the most effective PET amongst the set we tested. We find that some PETs have inconsistent behaviors, which can do more harm than good.

cs.CR↗

arXiv2018

Cybercasing 2.0: You Get What You Pay For

Under U.S. law, marketing databases exist under almost no legal restrictions concerning accuracy, access, or confidentiality. We explore the possible (mis)use of these databases in a criminal context by conducting two experiments. First, we show how this data can be used for "cybercasing" by using this data to resolve the physical addresses of individuals who are likely to be on vacation. Second, we evaluate the utility of a "bride to be" mailing list augmented with data obtained by searching both Facebook and a bridal registry aggregator. We conclude that marketing data is not necessarily harmless and can represent a fruitful target for criminal misuse.

cs.CR↗

arXiv2018

The Accuracy of the Demographic Inferences Shown on Google's Ad Settings

Google's Ad Settings shows the gender and age that Google has inferred about a web user. We compare the inferred values to the self-reported values of 501 survey participants. We find that Google often does not show an inference, but when it does, it is typically correct. We explore which usage characteristics, such as using privacy enhancing technologies, are associated with Google's accuracy, but found no significant results.

cs.CR↗

arXiv2018

Correspondences between Privacy and Nondiscrimination: Why They Should Be Studied Together

Privacy and nondiscrimination are related but different. We make this observation precise in two ways. First, we show that both privacy and nondiscrimination have two versions, a causal version and a statical associative version, with each version corresponding to a competing view of the proper goal of privacy or nondiscrimination. Second, for each version, we show that a difference between the privacy edition of the version and the nondiscrimination edition of the version is related to the difference between Bayesian probabilities and frequentist probabilities. In particular, privacy admits both Bayesian and frequentist interpretations whereas nondiscrimination is limited to the frequentist interpretation. We show how the introduced correspondence allows results from one area of research to be used for the other.

cs.CR↗

arXiv2018

A Bestiary of Blocking: The Motivations and Modes behind Website Unavailability

This paper examines different reasons the websites may vary in their availability by location. Prior works on availability mostly focus on censorship by nation states. We look at three forms of server-side blocking: blocking visitors from the EU to avoid GDPR compliance, blocking based upon the visitor's country, and blocking due to security concerns. We argue that these and other forms of blocking warrant more research.

cs.NI↗

arXiv2018

Exploring Server-side Blocking of Regions

One of the Internet's greatest strengths is the degree to which it facilitates access to any of its resources from users anywhere in the world. However, users in the developing world have complained of websites blocking their countries. We explore this phenomenon using a measurement study. With a combination of automated page loads, manual checking, and traceroutes, we can say, with high confidence, that some websites do block users from some regions. We cannot say, with high confidence, why, or even based on what criteria, they do so except for in some cases where the website states a reason. We do report qualitative evidence that fears of abuse and the costs of serving requests to some regions may play a role.

cs.NI↗

arXiv2016

Reviewer Integration and Performance Measurement for Malware Detection

We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource. We demonstrate that even in small numbers, reviewers can vastly improve the system's ability to keep pace with evolving threats. We conduct our evaluation on a sample of VirusTotal submissions spanning 2.5 years and containing 1.1 million binaries with 778GB of raw feature data. Without reviewer assistance, we achieve 72% detection at a 0.5% false positive rate, performing comparable to the best vendors on VirusTotal. Given a budget of 80 accurate reviews daily, we improve detection to 89% and are able to detect 42% of malicious binaries undetected upon initial submission to VirusTotal. Additionally, we identify a previously unnoticed temporal inconsistency in the labeling of training datasets. We compare the impact of training labels obtained at the same time training data is first seen with training labels obtained months later. We find that using training labels obtained well after samples appear, and thus unavailable in practice for current training data, inflates measured detection by almost 20 percentage points. We release our cluster-based implementation, as well as a list of all hashes in our evaluation and 3% of our entire dataset.

cs.CR↗

arXiv2015

Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination

To partly address people's concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google's ads, and Ad Settings interact. AdFisher can run browser-based experiments and analyze data using machine learning and significance tests. Our tool uses a rigorous experimental design and statistical analysis to ensure the statistical soundness of our results. We use AdFisher to find that the Ad Settings was opaque about some features of a user's profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse changed the ads shown but not the settings page. We also found that setting the gender to female resulted in getting fewer instances of an ad related to high paying jobs than setting it to male. We cannot determine who caused these findings due to our limited visibility into the ad ecosystem, which includes Google, advertisers, websites, and users. Nevertheless, these results can form the starting point for deeper investigations by either the companies themselves or by regulatory bodies.

cs.CR↗

arXiv2014

On Modeling the Costs of Censorship

We argue that the evaluation of censorship evasion tools should depend upon economic models of censorship. We illustrate our position with a simple model of the costs of censorship. We show how this model makes suggestions for how to evade censorship. In particular, from it, we develop evaluation criteria. We examine how our criteria compare to the traditional methods of evaluation employed in prior works.

cs.CR↗

arXiv2014

A Methodology for Information Flow Experiments

Information flow analysis has largely ignored the setting where the analyst has neither control over nor a complete model of the analyzed system. We formalize such limited information flow analyses and study an instance of it: detecting the usage of data by websites. We prove that these problems are ones of causal inference. Leveraging this connection, we push beyond traditional information flow analysis to provide a systematic methodology based on experimental science and statistical analysis. Our methodology allows us to systematize prior works in the area viewing them as instances of a general approach. Our systematic study leads to practical advice for improving work on detecting data usage, a previously unformalized area. We illustrate these concepts with a series of experiments collecting data on the use of information by websites, which we statistically analyze.

cs.CR↗

arXiv2011

On the Semantics of Purpose Requirements in Privacy Policies

Privacy policies often place requirements on the purposes for which a governed entity may use personal information. For example, regulations, such as HIPAA, require that hospital employees use medical information for only certain purposes, such as treatment. Thus, using formal or automated methods for enforcing privacy policies requires a semantics of purpose requirements to determine whether an action is for a purpose or not. We provide such a semantics using a formalism based on planning. We model planning using a modified version of Markov Decision Processes, which exclude redundant actions for a formal definition of redundant. We use the model to formalize when a sequence of actions is only for or not for a purpose. This semantics enables us to provide an algorithm for automating auditing, and to describe formally and compare rigorously previous enforcement methods.

cs.CR↗

arXiv2011

Formal Verification of Differential Privacy for Interactive Systems

Differential privacy is a promising approach to privacy preserving data analysis with a well-developed theory for functions. Despite recent work on implementing systems that aim to provide differential privacy, the problem of formally verifying that these systems have differential privacy has not been adequately addressed. This paper presents the first results towards automated verification of source code for differentially private interactive systems. We develop a formal probabilistic automaton model of differential privacy for systems by adapting prior work on differential privacy for functions. The main technical result of the paper is a sound proof technique based on a form of probabilistic bisimulation relation for proving that a system modeled as a probabilistic automaton satisfies differential privacy. The novelty lies in the way we track quantitative privacy leakage bounds using a relation family instead of a single relation. We illustrate the proof technique on a representative automaton motivated by PINQ, an implemented system that is intended to provide differential privacy. To make our proof technique easier to apply to realistic systems, we prove a form of refinement theorem and apply it to show that a refinement of the abstract PINQ automaton also satisfies our differential privacy definition. Finally, we begin the process of automating our proof technique by providing an algorithm for mechanically checking a restricted class of relations from the proof technique.

cs.CR↗