Kamalika Chaudhuri

Multi-view clustering via canonical correlation analysis

Clustering data in high dimensions is believed to be a hard problem in general. A number of efficient clustering algorithms developed in recent years address this problem by projecting the data into a lower-dimensional subspace, e.g. via Principal Components Analysis (PCA) or random projections, before clustering. Here, we consider constructing such projections using multiple views of the data, via Canonical Correlation Analysis (CCA). Under the assumption that the views are un-correlated given the cluster label, we show that the separation conditions required for the algorithm to be successful are significantly weaker than prior results in the literature. We provide results for mixtures of Gaussians and mixtures of log concave distributions. We also provide empirical support from audio-visual speaker clustering (where we desire the clusters to correspond to speaker ID) and from hierarchical Wikipedia document clustering (where one view is the words in the document and the other is the link structure).

Selfish caching in distributed systems: a game-theoretic analysis

We analyze replication of resources by server nodes that act selfishly, using a game-theoretic approach. We refer to this as the selfish caching problem. In our model, nodes incur either cost for replicating resources or cost for access to a remote replica. We show the existence of pure strategy Nash equilibria and investigate the price of anarchy, which is the relative cost of the lack of coordination. The price of anarchy can be high due to undersupply problems, but with certain network topologies it has better bounds. With a payment scheme the game can always implement the social optimum in the best case by giving servers incentive to replicate.

Paths, trees, and minimum latency tours

We give improved approximation algorithms for a variety of latency minimization problems. In particular, we give a 3.59-approximation to the minimum latency problem, improving on previous algorithms by a multiplicative factor of 2. Our techniques also give similar improvements for related problems like k-traveling repairmen and its multiple depot variant. We also observe that standard techniques can be used to speed up the previous and this algorithm by a factor of O/sup /spl tilde//(n).

iDASH: integrating data for analysis, anonymization, and sharing

iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool development and data-sharing capabilities in a private Health Insurance Portability and Accountability Act (HIPAA)-certified cloud. Driving Biological Projects, which span different biological levels (from molecules to individuals to populations) and focus on various health conditions, help guide research and development within this Center. Furthermore, training and dissemination efforts connect the Center with its stakeholders and educate data owners and data consumers on how to share and use clinical and biological data. Through these various mechanisms, iDASH implements its goal of providing biomedical and behavioral researchers with access to data, software, and a high-performance computing environment, thus enabling them to generate and test new hypotheses.

When random sampling preserves privacy

Many organizations such as the U.S. Census publicly release samples of data that they collect about private citizens. These datasets are first anonymized using various techniques and then a small sample is released so as to enable “do-it-yourself” calculations. This paper investigates the privacy of the second step of this process: sampling. We observe that rare values – values that occur with low frequency in the table – can be problematic from a privacy perspective. To our knowledge, this is the first work that quantitatively examines the relationship between the number of rare values in a table and the privacy in a released random sample. If we require e-privacy (where the larger e is, the worse the privacy guarantee) with probability at least 1 – δ, we say that a value is rare if it occurs in at most

Stochastic gradient descent with differentially private updates

\tilde{O}(\frac{1}{\epsilon})

Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data

rows of the table (ignoring log factors). If there are no rare values, then we establish a direct connection between sample size that is safe to release and privacy. Specifically, if we select each row of the table with probability at most e then the sample is O(e)-private with high probability. In the case that there are t rare values, then the sample is

On the tandem duplication-random loss model of genome rearrangement

\tilde{O}(\epsilon \delta /t)

What would edmonds do? augmenting paths and witnesses for degree-bounded MSTs

-private with probability at least 1–δ.

Value-maximizing deadline scheduling and its application to animation rendering

Differential privacy is a recent framework for computation on sensitive data, which has shown considerable promise in the regime of large datasets. Stochastic gradient methods are a popular approach for learning in the data-rich regime because they are computationally tractable and scalable. In this paper, we derive differentially private versions of stochastic gradient descent, and test them empirically. Our results show that standard SGD experiences high variability due to differential privacy, but a moderate increase in the batch size can improve performance significantly.