Privacy through Accountability: A Computer Science Perspective

Datta, Anupam

doi:10.1007/978-3-319-04483-5_5

Cited by 11 publications

(7 citation statements)

References 16 publications

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“…This is the case, for instance, of statistical frameworks (Karr and Reiter, 2014), the model of differential privacy (Dwork, 2006) and other formal methods for enforcing privacy policy (e.g. see (Datta, 2014)).…”

Section: The Current Debatementioning

confidence: 99%

Community interpreting, translation, and technology

Mellinger

Покорн

2018

TIS

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The focus of ETHI-CA2 spans ethical aspects around the entire processing pipeline from speech and language, as well as multimodal resource collection and annotation, to system development and application. In the recent time of ever-more collection "in the wild" of individual and personal multimodal and multi-sensorial "Big Data", crowd-sourced annotation by large groups of individuals with often unknown reliability and high subjectivity, and "deep" and autonomous learning with limited transparency of what is being learnt, and how applications such as in health or robotics depending on such data may behave, ethics have become more crucial than ever in the field of language and multimodal resources. This makes ethics a key concern of the LREC community. There is, however, a surprising if not shocking white spot in the landscape of workshops, special session, or journal special issues in this field, which ETHI-CA2 aims to fill in. The goal is thus to connect individuals ranging across LREC's fields of interest such as humanmachine and robot-and computer-mediated human-human interaction and communication, affective, behavioral, and social computing whose work touches on crucial ethical issues (e.g. privacy, traceability, explainability, evaluation, responsibility, etc.). According systems increasingly interact with and exploit data from humans of all ranges (e.g. children, adults, vulnerable populations) including non-verbal and verbal data occurring in a variety of real-life contexts (e.g. at home, the hospital, on the phone, in the car, classroom, or public transportation) and act as assistive and partially instructive technologies, companions, and/or commercial or even decision-making systems. Obviously, an immense responsibility lies at the different ends from data recording, labeling, and storage, to its processing and usage. Motivation Emerging interactive systems have changed the way we connect with our machines, modifying how we socialize, our reasoning capabilities, and our behavior. These areas inspire critical questions centering on the ethics, the goals, and the deployment of innovative products that can change our lives and society. Many current systems operate on private user data, including identifiable information, or data that provides insight into an individual's life routine. The workshop will provide discussions about user consent and the notion of informed data collection. Cloud-based storage systems have grown in popularity as the scope of user-content and usergenerated content has greatly increased in size. The workshop will provide discussions on best practices for data annotation and storage and evolving views on data ownership. Systems have become increasingly capable of mimicking human behavior through research in affective computing. These systems have provided demonstrated utility, for interactions with vulnerable populations (e.g. the elderly, children with autism). The workshop will provide discussions on considerations for vulnerable populations. The common mantra for assistive technol...

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Section: The Current Debatementioning

confidence: 99%

Community interpreting, translation, and technology

Mellinger

Покорн

2018

TIS

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“…The technique of using predefined probe queries is borrowed from black-box testing. Modelling an adversary as a black-box, where internal details of recommender systems algorithms and settings are unknown to users, is mentioned in [3] and [4].…”

Section: Related Workmentioning

confidence: 99%

“…The position with respect to handling of data collected by online recommender systems is less clear. In [3], the author reviews computational approaches to specification and enforcement of privacy policies at large scale.…”

Section: Related Workmentioning

confidence: 99%

Plausible Deniability in Web Search—From Detection to Assessment

Aonghusa

Leith

2018

IEEE Trans.Inform.Forensic Secur.

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Abstract-Web personalisation uses what systems know about us to create content targeted at our interests. When unwanted personalisation suggests we are interested in sensitive or embarrassing topics a natural reaction is to deny interest. This is a practical response only if denial of our interest is credible or plausible. Adopting a definition of plausible deniability in the usual sense of "on the balance of probabilities", we develop a practical and scalable tool called PDE allowing a user to decide when their ability to plausibly deny interest in sensitive topics is compromised. We show that threats to plausible deniability are readily detectable for all topics tested in an extensive testing program. Of particular concern is observation of threats to deniability of interest in topics related to health and sexual preferences. We show this remains the case when attempting to disrupt search engine learning through noise query injection and click obfuscation. We design a defence technique exploiting uninteresting, proxy topics and show that it provides a more effective defence of plausible deniability in our experiments.

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“…Model checking to verify that HIPAA (the federal Health Insurance Portability and Accountability Act) is being followed [23].…”

Section: Privacy and Securitymentioning

confidence: 99%

The NIST data science initiative

Dorr

Greenberg

Fontana

et al. 2015

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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We examine foundational issues in data science including current challenges, basic research questions, and expected advances, as the basis for a new Data Science Initiative and evaluation series, introduced by the National Institute of Standards and Technology (NIST) in the fall of 2015. The evaluations will facilitate research efforts, collaboration, leverage shared infrastructure, and effectively address cross-cutting challenges faced by diverse data science communities. The evaluations will have multiple research tracks championed by members of the data science community, and will enable rigorous comparison of approaches through common tasks, datasets, metrics, and shared research challenges. The tracks will measure several different data science technologies in a wide range of fields, starting with a pre-pilot. In addition to developing data science evaluation methods and metrics, it will address computing infrastructure, standards for an interoperability framework, and domain-specific examples.

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Privacy through Accountability: A Computer Science Perspective

Cited by 11 publications

References 16 publications

Community interpreting, translation, and technology

Community interpreting, translation, and technology

Plausible Deniability in Web Search—From Detection to Assessment

The NIST data science initiative

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