Speakers
Prof. Krishna Gummadi, Tenured Faculty, Max Planck Institute for Software Systems, Germany
Title: Privacy and Fairness Concerns with PII-based Targeted Advertising on Social Media
Abstract: All popular social media sites like Facebook, Twitter, and Pinterest are funded by advertising, and the detailed user data that these sites collect make them attractive platforms for advertisers. Historically, these advertising platforms allowed advertisers to target users with certain attributes, but not to target users directly. Recently, most advertising platforms have begun allowing advertisers to target users directly by uploading the personal information of the users who they wish to advertise to (e.g., their names, email addresses, phone numbers, etc). Such targeting is referred to as custom audience targeting. In this talk, I will discuss numerous privacy and fairness concerns that arise with such custom audience targeting on the Facebook ad platform. I will show how custom audience targeting would allow malicious advertisers to leverage existing public records (e.g., voter records) for discriminatory advertising (i.e., excluding people of a certain race), and how this type of discrimination is significantly more difficult for Facebook to detect automatically. We also find that the custom audiences can be abused by malicious advertisers to learn about hundreds of demographic, behavioral, and interest attributes of a Facebook user even with limited knowledge about their PII like their email addresses or phone numbers. Finally, we find that users generally have no control over their data that is used to create custom audiences. Overall, our results indicate that advertising platforms need to more carefully consider the privacy and fairness concerns that arise out of custom audience targeting.
Bio: Dr. Krishna Gummadi is a tenured faculty member and head of the Networked Systems research group at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. Krishna's research interests are in the measurement, analysis, design, and evaluation of complex Internet-scale systems. His current projects focus on understanding and building social computing systems. Specifically, they tackle the challenges associated with (i) assessing the credibility of information shared by anonymous online crowds, (ii) understanding and controlling privacy risks for users sharing data on online forums, (iii) understanding, predicting and influencing human behaviors on social media sites (e.g., viral information diffusion), and (iv) enhancing fairness and transparency of machine (data-driven) decision making in social computing systems. Krishna's work on online social networks, Internet access networks, and peer-to-peer systems has led to a number of widely cited papers and award papers at IW3C2's WWW, NIPS's ML & Law Symposium, ACM's COSN, ACM/Usenix's SOUPS, AAAI's ICWSM, Usenix's OSDI, ACM's SIGCOMM IMC, and SPIE's MMCN conferences. He has also co-chaired AAAI's ICWSM 2016, IW3C2 WWW 2015, ACM COSN 2014, and ACM IMC 2013 conferences.
Title: Privacy and Fairness Concerns with PII-based Targeted Advertising on Social Media
Abstract: All popular social media sites like Facebook, Twitter, and Pinterest are funded by advertising, and the detailed user data that these sites collect make them attractive platforms for advertisers. Historically, these advertising platforms allowed advertisers to target users with certain attributes, but not to target users directly. Recently, most advertising platforms have begun allowing advertisers to target users directly by uploading the personal information of the users who they wish to advertise to (e.g., their names, email addresses, phone numbers, etc). Such targeting is referred to as custom audience targeting. In this talk, I will discuss numerous privacy and fairness concerns that arise with such custom audience targeting on the Facebook ad platform. I will show how custom audience targeting would allow malicious advertisers to leverage existing public records (e.g., voter records) for discriminatory advertising (i.e., excluding people of a certain race), and how this type of discrimination is significantly more difficult for Facebook to detect automatically. We also find that the custom audiences can be abused by malicious advertisers to learn about hundreds of demographic, behavioral, and interest attributes of a Facebook user even with limited knowledge about their PII like their email addresses or phone numbers. Finally, we find that users generally have no control over their data that is used to create custom audiences. Overall, our results indicate that advertising platforms need to more carefully consider the privacy and fairness concerns that arise out of custom audience targeting.
Bio: Dr. Krishna Gummadi is a tenured faculty member and head of the Networked Systems research group at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. Krishna's research interests are in the measurement, analysis, design, and evaluation of complex Internet-scale systems. His current projects focus on understanding and building social computing systems. Specifically, they tackle the challenges associated with (i) assessing the credibility of information shared by anonymous online crowds, (ii) understanding and controlling privacy risks for users sharing data on online forums, (iii) understanding, predicting and influencing human behaviors on social media sites (e.g., viral information diffusion), and (iv) enhancing fairness and transparency of machine (data-driven) decision making in social computing systems. Krishna's work on online social networks, Internet access networks, and peer-to-peer systems has led to a number of widely cited papers and award papers at IW3C2's WWW, NIPS's ML & Law Symposium, ACM's COSN, ACM/Usenix's SOUPS, AAAI's ICWSM, Usenix's OSDI, ACM's SIGCOMM IMC, and SPIE's MMCN conferences. He has also co-chaired AAAI's ICWSM 2016, IW3C2 WWW 2015, ACM COSN 2014, and ACM IMC 2013 conferences.
Prof. Amitava Das , Assistant Professor, IIIT Sri City
Title: Understanding Psycho-Sociological Vulnerability of ISIS Patronizers in Twitter
Abstract: In the last decade we have witnessed the birth and rapid growth of Wikipedia, Google, Facebook, YouTube, Twitter, and numerous other marvels of the digital age. In addition to changing the way we live, these tools have fundamentally changed the way that we can learn about the social world. We can now collect data about human behavior on a scale never before possible and with tremendous granularity and precision. The evolution of social media has created many new opportunities for information access, but also bring into many new unseen challenges, making it one of the prime present-day research areas. Social media has become the mainstream recruitment media for terrorist recruitment. The Islamic State of Iraq and Syria (ISIS) is a Salafi-jihadist militant group that has made extensive use of online social media platforms to promulgate its ideologies and evokes many individuals to support the organization. The psycho-sociological background of an individual plays a crucial role in determining his/her vulnerability of being lured into joining the organization and indulge in terrorist activities since his/her behavior largely depends on the society s/he was brought up in. We have analyzed five sociological aspects – personality, values & ethics, optimism/pessimism, age, and gender to understand the psycho-sociological vulnerability of individuals over Twitter. Experimental results suggest that psycho-sociological aspects indeed act as a foundation to discover and differentiate between prominent and unobtrusive users on Twitter.
Bio: Dr. Amitava Das is working as an Assistant Professor in the Department of Computer Science & Engineering at the IIIT Sri City, Andhra Pradesh, India. He has obtained Ph.D. (Engineering) from Jadavpur University, India. During his doctoral study, he worked for an Indo-Japan collaborative project entitled “Sentiment Analysis where AI meets Psychology” with the Tokyo Institute of Technology, Japan. His research interests broadly span over three areas and more specifically their intersection: human language, mind/cognition, and artificial intelligence, technically known as Natural Language Processing (NLP). During his doctoral study, his primary research focus was Sentiment Analysis / Opinion Mining. He is actively working on language technologies and data science since last 10 years and published in a wide spectrum of the subject. Currently, he is actively working in these following areas: computational social science, code-mixing in the social media text, and social media data visualization in virtual reality
Title: Understanding Psycho-Sociological Vulnerability of ISIS Patronizers in Twitter
Abstract: In the last decade we have witnessed the birth and rapid growth of Wikipedia, Google, Facebook, YouTube, Twitter, and numerous other marvels of the digital age. In addition to changing the way we live, these tools have fundamentally changed the way that we can learn about the social world. We can now collect data about human behavior on a scale never before possible and with tremendous granularity and precision. The evolution of social media has created many new opportunities for information access, but also bring into many new unseen challenges, making it one of the prime present-day research areas. Social media has become the mainstream recruitment media for terrorist recruitment. The Islamic State of Iraq and Syria (ISIS) is a Salafi-jihadist militant group that has made extensive use of online social media platforms to promulgate its ideologies and evokes many individuals to support the organization. The psycho-sociological background of an individual plays a crucial role in determining his/her vulnerability of being lured into joining the organization and indulge in terrorist activities since his/her behavior largely depends on the society s/he was brought up in. We have analyzed five sociological aspects – personality, values & ethics, optimism/pessimism, age, and gender to understand the psycho-sociological vulnerability of individuals over Twitter. Experimental results suggest that psycho-sociological aspects indeed act as a foundation to discover and differentiate between prominent and unobtrusive users on Twitter.
Bio: Dr. Amitava Das is working as an Assistant Professor in the Department of Computer Science & Engineering at the IIIT Sri City, Andhra Pradesh, India. He has obtained Ph.D. (Engineering) from Jadavpur University, India. During his doctoral study, he worked for an Indo-Japan collaborative project entitled “Sentiment Analysis where AI meets Psychology” with the Tokyo Institute of Technology, Japan. His research interests broadly span over three areas and more specifically their intersection: human language, mind/cognition, and artificial intelligence, technically known as Natural Language Processing (NLP). During his doctoral study, his primary research focus was Sentiment Analysis / Opinion Mining. He is actively working on language technologies and data science since last 10 years and published in a wide spectrum of the subject. Currently, he is actively working in these following areas: computational social science, code-mixing in the social media text, and social media data visualization in virtual reality
Dr. Manish Gupta, Senior Researcher, Microsoft Research India
Title: Basics of social network analysis
Abstract: In this talk, I will talk about important properties of real world social networks including degree distribution, clustering coefficient, small diameter, and spectrum of connected components. Further, I will talk about models for generating synthetic networks. These include random graphs model, Watt-Strogatz model, Kleinberg's model, small world model, preferential attachment model, copying model and forest fire models.
Bio: Dr. Manish Gupta is a Senior Applied Researcher at Microsoft India R&D Private Limited at Hyderabad, India. He is also an Adjunct Faculty at International Institute of Information Technology, Hyderabad and a visiting faculty at Indian School of Business, Hyderabad. He received his Masters in Computer Science from IIT Bombay in 2007 and his Ph.D. from the University of Illinois at Urbana-Champaign in 2013. Before this, he worked for Yahoo! Bangalore for two years. His research interests are in the areas of web mining, data mining and information retrieval. He has published more than 50 research papers in reputed referred journals and conferences. He has also co-authored two books: one on Outlier Detection for Temporal Data and another one on Information Retrieval with Verbose Queries.
Title: Basics of social network analysis
Abstract: In this talk, I will talk about important properties of real world social networks including degree distribution, clustering coefficient, small diameter, and spectrum of connected components. Further, I will talk about models for generating synthetic networks. These include random graphs model, Watt-Strogatz model, Kleinberg's model, small world model, preferential attachment model, copying model and forest fire models.
Bio: Dr. Manish Gupta is a Senior Applied Researcher at Microsoft India R&D Private Limited at Hyderabad, India. He is also an Adjunct Faculty at International Institute of Information Technology, Hyderabad and a visiting faculty at Indian School of Business, Hyderabad. He received his Masters in Computer Science from IIT Bombay in 2007 and his Ph.D. from the University of Illinois at Urbana-Champaign in 2013. Before this, he worked for Yahoo! Bangalore for two years. His research interests are in the areas of web mining, data mining and information retrieval. He has published more than 50 research papers in reputed referred journals and conferences. He has also co-authored two books: one on Outlier Detection for Temporal Data and another one on Information Retrieval with Verbose Queries.