Scroll down for some amazing photos of the day, which also included a photo backdrop and Halloween tunes!
Other CIS-affiliated Halloween events include:
The Penn Society of Women Engineers Meet and Greet – Levine Lobby, October 29, 4-5pm Come take a break from studying, meet other students and enjoy some arts and crafts and insomnia cookies.
CIS Faculty and Postdoctoral Fellow Halloween TGIF – Quain Courtyard, October 29, 5-8pm There will be a Halloween Costume Contest with gift card prizes for winners! There will also be a pumpkin carving event, food, and an expanded selection of alcoholic and non-alcoholic drinks.
However, according to Department of Computer and Information Science Professor Andreas Haeberlen, one of the main reasons contact tracing wasn’t relatively more successful is simple: people don’t’ feel comfortable sharing their information.
“It’s really scary to think of people knowing all the things that you type in your phone,” said Haeberlen. “Like what you’ve had for breakfast, or your medical information, or where you’ve been all day or who you’ve met. All of that data is super super sensitive.”
Haeberlen, whose research centers distributed systems, networking, security, and privacy, believes that differential privacy could be the solution.
“Differential privacy is a way to purpose private information so that you can really guarantee that somebody can’t later learn something sensitive from this information,” said Haeberlen. “[It] has a very solid mathematical foundation.”
The National Institute of Science and Technology defines differential privacy in terms of mathematical qualification. “It is not a specific process, but a property that a process can have,” said NIST on their website. “For example, it is possible to prove that a specific algorithm ‘satisfies’ differential privacy.”
And so we might assert that, if an analysis of a database without Joe Citizen’s individual data and an analysis of a database with Joe Citizen’s individual data yield indistinguishable results, then differential privacy is satisfied. “This implies that whoever sees the output won’t be able to tell whether or not Joe’s data was used, or what Joe’s data contained,” said NIST on their site.
Haeberlen insists that, with widespread application of differential privacy, user trust is not only no longer a barrier, but that it is not necessarily required. Surrendering our sensitive information to large corporations such as Apple would no longer require a leap of faith.
Building the tools
A popular industry standard of cybersecurity involves adding imprecision into results to purposefully skew them, and thus protect individual user data. Challenges to this application, according to Haeberlen, include the ongoing debate among experts about whether it satisfies differential privacy specifications, and its lack of scalability.
“Fuzzi: A Three-Level Logic for Differential Privacy,” a paper by Haeberlen and fellow researchers Edo Roth, Hengchu Zhang, Benjamin C. Pierce and Aaron Roth, is one of many of Haeberlen’s oeuvre that focuses on developing tools that can do the work for us. The paper presents a prototype called Fuzzi, whose top level of operational logic “is a novel sensitivity logic adapted from the linear-logic-inspired type system of Fuzz, a differentially private functional language,” according to the abstract.
Essentially, a researcher would input data into the tool, define what that data means, and specify what data output they’re searching for. The tool would be able to state if that output satisfies differential privacy specifications, and, if not, what amount of imprecision would need to be added in order to meet specifications.
“The way that we did that was by baking differential privacy into a programming language,” said Haeberlen. “As a practitioner you don’t have to understand what differential privacy is, you also don’t have to be able to prove it.”
In the world of science, imprecision usually means error and gross miscalculation. However, in the more specific realm of differential privacy, imprecision equals security.
“Imprecision is good because it causes the adversary to make mistakes,” said Haeberlen. In this case, the “adversary” is any person or system trying to gain access to sensitive information.
All tools developed by Haeberlen and his team have been made available under open-source license, and companies such as Uber and Facebook are currently releasing data sets using differential privacy.
On Friday, October 1, 2021, the University of Pennsylvania’s School of Engineering and Applied Science held a groundbreaking ceremony for its new data science building and unveiled the building’s official name, Amy Gutmann Hall, honoring Penn’s President. Amy Gutmann is the eighth and longest-serving President in Penn’s history, leading the University since 2004; her term will conclude at the end of this academic year.
Amy Gutmann Hall will serve as a hub for cross-disciplinary collaborations that harness expertise, research, and data across Penn’s 12 schools and numerous academic centers. Upon completion, it will centralize resources that will advance the work of scholars across a wide variety of fields while making the tools and concepts of data analysis more accessible to the entire Penn community.
“I am thrilled Penn Engineering’s new data science building will honor Dr. Gutmann’s remarkable legacy at Penn,” said Vijay Kumar, the Nemirovsky Family Dean of Penn Engineering. “Her Penn Compact and the principles of inclusion, innovation, and impact influenced the school’s strategic priorities from which the plan for a data science building emerged. This revolutionary new facility will create a centralized home for data science research and provide collaborative and accessible space for our faculty and students, as well as the Philadelphia community.”
The 116,000-square-foot, six-floor building will be located at the northeast corner of 34th and Chestnut Streets. Planned academic features include a data science hub, the translational and outreach arm of Penn Engineering in the area of data science and artificial intelligence; research centers for new socially aware data science methodologies and novel, bio-inspired paradigms for computing; and laboratories that will develop data-driven, innovative approaches for safer and more cost-effective health care.
The impressive building is the design of executive architects Lake/Flato, with KSS Architects serving as associate architects. The building’s architecture will signify the future and the dynamic shift from the traditional to the digital. The facility is planned to be the first mass timber building in Philadelphia and will be designed sustainably.
Construction will begin in spring 2022 and is slated for completion in 2024.