This Workshop is intended to be a generative space for participants to create a module on ethics in a Computer Science course of their choice. Participants will choose an existing course in the typical CS curriculum; and coalesce around common themes to create a module that integrates critical reflection on ethical choices and societal impact of computing with the practice of computing. The workshop will offer a space to collaboratively work on designing an ethics module for a CS course chosen by the participants. The ethics module would integrate teaching specific concepts and practices in computing alongside a critical reflection of these practices. Ideally, these modules would be flexible enough to fit into curricula at various institutions.

CS majors are certainly not disappearing, but the interest and demand for computing from non-CS majors is a far larger part of our future, as members of CS departments, than those on the disciplinary-major path. As computing evolves from valuable specialty to a professional literacy, CS departments face several challenges. How do we support both the traditional CS-major path and provide computing curriculum to a far broader audience? How do we partner with sibling departments in order to foster their sense of ownership and identity with computing's mindsets and toolsets? And, perhaps especially poignant in 2018, how do we invest energy into the CS-for-All future during a time that demand in our major pathway is at historic highs? This workshop distills the experiences of three faculty members who have tried to answer these questions within the context of their departments. The presenters will share the results of those experiments, will offer hands-on exploration of a representative subset of the curricular materials, and will scaffold a strategic discussion of CS's future and identity in the era of "CS for All."

As a consequence of living in a data ecosystem, we often relinquish personal information to be used in contexts in which we have no control. In this paper, we begin to examine the usability of differential privacy, a mechanism that proposes to promise privacy with a mathematical "proof" to the data donor. Do people trust this promise and adjust their privacy decisions if the interfaces through which they interact make differential privacy less opaque? In a study with 228 participants, we measured comfort, understanding, and trust using a variant of differential privacy known as Randomized Response Technique (RRT). We found that allowing individuals to see the amount of obfuscation applied to their responses increased their trust in the privacy-protecting mechanism. However, participants who associated obfuscating privacy mechanisms with deception did not make the "safest" privacy decisions, even as they demonstrated an understanding of RRT. We demonstrate that prudent privacy-related decisions can be cultivated with simple explanations of usable privacy.

Understanding of computing skills and concepts is no longer viewed as optional, but is knowledge that is required to progress in several fields outside of computer science, even outside STEM fields. Despite the need for this knowledge, non-STEM majors' participation in computing courses is still lacking. This is especially true for women and other minority groups. In our work, our main motivation was creating a course at our institution, Bucknell University to make computer science more accessible to non-STEM majors on our campus. We adopted an approach of situating course design in the experiences and perspectives of undergraduate students themselves. To achieve this, the course curriculum and materials were mainly designed and created by three undergraduate students, all below the junior level, who connected course topics to a wide variety of topics outside computing. This project is based on the precept that students from varied disciplinary backgrounds and intermediate computing knowledge can provide a unique lens to articulate the needs of their fellow students. In order to serve the purpose of emphasizing creativity and society, we chose a sketch based programming language, namely p5.js (a JavaScript library which brings Processing to the web), which gives students the possibility and flexibility to create projects that are directly relevant to their own interests, while gaining a basic conceptual understanding of Computer Science

Discusses the ramifications from a privacy and network security point of view given today's systems that offer access to huge amounts of data and database systems. These information and data sharing interactions capture varied aspects of our lives, such as our shopping behavior, our mental and physical health, our likes and dislikes, political and religious opinions, products we shop for, how we drive our cars, how much energy we use, and even more nebulous things such as our hopes and aspirations. Credit card companies record every purchase we make, with the time and place of purchase; companies like Amazon and Netflix not only record our purchases but also our interest in things we merely peruse on their websites. Loyalty cards and digital profiles of our purchases even in brick-and mortar stores such as Target and Walmart and our local supermarkets create a record of all our purchases, easily being able to infer our shopping patterns.

Models of vehicle mobility have applicability in fields such as urban planning and environmental studies. Big Data, specifically, spatio-temporal data, enable novel ways of constructing such models, however privacy remains an issue. Unfortunately, there is no known way to anonymize location data since spatio-temporal data is highly unique to individuals and robust to changes over extended periods of time. Unlike simple anonymization, differentially private summaries of data provide a mathematical guarantee of privacy, while maintaining the utility of the mobility data. Using a dataset from Telecom Italia's Big Data Challenge, we construct a differentially private model of tollgate traffic in the city of Milan. We then use the model to generate synthetic tollgate data and compare its accuracy on various metrics to that of real data. Our findings show that it is possible to create an accurate, differentially private mobility model from this location data that preserves important characteristics of the original data.

Models of human mobility have broad applicability in urban planning, ecology, epidemiology, and other fields. Starting with Call Detail Records (CDRs) from a cellular telephone network that have gone through a straightforward anonymization procedure, the prior WHERE modeling approach produces synthetic CDRs for a synthetic population. The accuracy of WHERE has been validated against billions of location samples for hundreds of thousands of cell phones in the New York and Los Angeles metropolitan areas. In this paper, we introduce DP-WHERE, which modifies WHERE by adding controlled noise to achieve differential privacy, a strict definition of privacy that makes no assumptions about the power or background knowledge of a potential adversary. We also present experiments showing that the accuracy of DP-WHERE remains close to that of WHERE and of real CDRs. With this work, we aim to enable the creation and possible release of synthetic models that capture the mobility patterns of real metropolitan populations while preserving privacy.

Consider fully dynamic data, where we track data as it gets inserted and deleted. There are well developed notions of private data analyses with dynamic data, for example, using differential privacy. We want to go beyond privacy, and consider privacy together with security, formulated recently as pan-privacy by Dwork et al. (ICS 2010). Informally, pan-privacy preserves differential privacy while computing desired statistics on the data, even if the internal memory of the algorithm is compromised (say, by a malicious break-in or insider curiosity or by fiat by the government or law).

A coding theory framework for related-key linear cryptanalytic attacks on block ciphers is presented. It treats linear cryptanalysis as communication over a low capacity channel, and a related key attack (RKA) as a concatenated code. It is used to show that an RKA, using n related keys generated from k independent ones, can improve the amortized cost - in number of plaintext-ciphertext pairs per key bit determined over that of k single key attacks, of any linear cryptanalysis, if k and n are large enough. The practical implications of this result are demonstrated through the design of an RKA, with k=5 and n=7, predicted to produce a 29% improvement for DES attacks that use an r-1 round approximation