Giulia C. Fanti
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Privacy-preserving information sharing
Ph.D. research

Recent years have brought ever-increasing levels of censorship and monitoring of electronic communications. The goal of this work is to design privacy-preserving tools that enable people to anonymously disseminate and consume content. In particular, I am interested in low-connectivity settings, such as when governments turn off key communication infrastructure like the Internet and/or cellular networks. I am also interested in private information search and retrieval (PIR, PSS), and how the research community can turn it into a practical privacy tool.

  • G. Fanti and P. Kairouz (co-first-authors), S. Oh, P. Viswanath, Spy vs. Spy: Rumor Source Obfuscation, to appear in SIGMETRICS, 2015
  • G. Fanti, V. Pihur, U. Erlingsson, Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries, 2015
  • G. Fanti, K. Ramchandran, Efficient, Multi-Server Private Information Retrieval over Unsynchronized Databases, Journal version under review, 2015
    [Allerton] [Journal Version]
  • A. Lerner, G. Fanti, J. Garcia, Y. Ben-David, B. Raghavan, Rangzen: Circumventing Government-Imposed Communication Blackouts, Under review, 2015
    [Tech Report]
    Media Coverage:
    [Berkeley Engineer] [SF Chronicle]
Signal processing for graph signals

Graph-structured data arises frequently in modern applications, including in social graphs, sensor networks, and biological networks. The goal of this work is to develop a framework for dealing with signals that are defined over arbitrary graphs, analogous to classical signal processing defined over regular domains (e.g. spatial grid, discrete-time).

  • V. Ekambaram, G. Fanti, B. Ayazifar, and K. Ramchandran, Multiresolution graph signal processing via circulant structures." IEEE DSP/SPE, 2013
  • V. Ekambaram, G. Fanti, B. Ayazifar, and K. Ramchandran, Critically-sampled perfect-reconstruction spline-wavelet filter banks for graph signals, Proc. GLOBESIP, 2013.
  • V. Ekambaram, G. Fanti, B. Ayazifar, and K. Ramchandran, Circulant structures and graph signal processing,. Proc. of ICIP, 2013.
Privacy-preserving media retrieval
M.S. research

Current content-based media search techniques can reveal a great deal of information to servers processing such requests. We propose and evaluate a scheme for privacy-preserving media retrieval that reveals no information about the client's query to the server. This could be useful in privacy-preserving surveillance systems, for instance.

  • G. Fanti, M. Finiasz, G. Friedland, and K. Ramchandran, Toward efficient, privacy-aware media classification on public databases, Proc. of ACM ICMR, 2014
  • G. Fanti, M. Finiasz, and K. Ramchandran, One-Way Private Media Search on Public Databases: The Role of Signal Processing, IEEE Signal Processing Magazine, 2013
    [paper] [pdf]
Wireless power transfer
Undergraduate research with Professor J.O. Mur-Miranda at Olin College of Engineering.
  • J.O. Mur-Miranda, G. Fanti, Y. Feng, K. Omanakuttan, R. Ongie, A. Setjoadi, and N. Sharpe, Wireless power transfer using weakly coupled magnetostatic resonators, Energy Conversion Congress and Exposition (ECCE), (2010)
  • J.O. Mur-Miranda and G. Fanti, Peak wireless power transfer using magnetically coupled series resonators, Proc. of IEEE EnergyCon, 2010
Uplink Femtocell Modeling, Spring 2012
Course project for EE224B (Wireless Communications) with Kangwook Lee

Current attempts at modeling femtocell capacity are very complex and depend heavily on assumptions about the distribution of users. We formulate a model that is significantly simplified while still capturing salient features of cell behaviour.

Latent Dirichlet Allocation for Geo-Tagging Flickr Photos, Fall 2011
Course project for EE281A (Statistical Learning Theory) with Rashmi K. V.

Significant attention is being paid to the problem of geotagging photos, or determining where they were taken based on content and metadata. We explore an LDA-based approach for estimating the city in which a photograph was taken.

Semidefinite Programming Relaxation for Localization in Non-Line-of-Sight Environments, 2011-2012
Technical Report with V. Ekambaran and K. Ramchandran

Sensor networks often have noisy distance estimates between neighboring entities. We explore an SDP relaxation for resolving these noisy distance estimates into precise location estimates. Specifically, we consider the case in which distance estimates are affected by strong positive bias, e.g. multipath.