In this digital age, people spend a significant portion of their lives online and this has led to an explosion of personal data of users due to their activities. Typically, this data is private and nobody else, except the user, is allowed to look at it. To provide better experience and assist users in their activities, it is critical to mine certain information from this data. This poses interesting and complex challenge from scalable information extraction point of view: building information extraction models where there is little data to learn from due to privacy constraints but need highly accurate models to run on a large amount of diverse data across different users. Anonymization of data is typically used to convert private data into publicly accessible data. But this may not always be feasible and may require complex differential privacy guarantees to be safe from any potential negative consequences. Further, the anonymization process needs to ensure that it retains sufficient information for modeling purposes post anonymization. Other techniques involve building extraction models using a small amount of seen (eyes-on) data with no privacy restrictions (hence, can be labeled) and a large amount of unseen (eyes-off) data which only a machine or a program can access. In this tutorial, we use emails as the canonical example of private data to explain in detail the challenges and solutions for scalable information extraction (IE) under privacy-aware constraints.

The EQUALITY problem is usually one’s first encounter with communication complexity and is one of the most fundamental problems in the field. Although its deterministic and randomized communication complexity were settled decades ago, we find several new things to say about the problem by focusing on three subtle aspects. The first is to consider the expected communication cost (at a worst-case input) for a protocol that uses limited interaction—i.e., a bounded number of rounds of communication—and whose error probability is zero or close to it. The second is to treat the false negative error rate separately from the false positive error rate. The third is to consider the information cost of such protocols. We obtain asymptotically optimal rounds-versus-cost tradeoffs for EQUALITY: both expected communication cost and information cost scale as Theta(log log ... log n), with r-1 logs, where r is the number of rounds. These bounds hold even when the false negative rate approaches 1. For the case of zero-error communication cost, we obtain essentially matching bounds, up to a tiny additive constant. We also provide some applications.

We consider the following fundamental communication problem - there is data that is distributed among servers, and the servers want to compute the intersection of their data sets, e.g., the common records in a relational database. They want to do this with as little communication and as few messages (rounds) as possible. They are willing to use randomization, and fail with a tiny probability. Given a protocol for computing the intersection, it can also be used to compute the exact Jaccard similarity, the rarity, the number of distinct elements, and joins between databases. Computing the intersection is at least as hard as the set disjointness problem, which asks whether the intersection is empty. Formally, in the two-server setting, the players hold subsets S, T ⊆ [n]. In many realistic scenarios, the sizes of S and T are significantly smaller than n, so we impose the constraint that |S|, |T| ≤ k. We study the minimum number of bits the parties need to communicate in order to compute the intersection set S ∩ T, given a certain number r of messages that are allowed to be exchanged. While O(k log (n/k)) bits is achieved trivially and deterministically with a single message, we ask what is possible with more than one message and with randomization. We give a smooth communication/round tradeoff which shows that with O(log* k) rounds, O(k) bits of communication is possible, which improves upon the trivial protocol by an order of magnitude. This is in contrast to other basic problems such as computing the union or symmetric difference, for which Ω(k log(n/k)) bits of communication is required for any number of rounds. For two players, known lower bounds for the easier problem of set disjointness imply our algorithms are optimal up to constant factors in communication and number of rounds. We extend our protocols to $m$-player protocols, obtaining an optimal O(mk) bits of communication with a similarly small number of rounds.

This paper makes three main contributions to the theory of communication complexity and stream computation. First, we present new bounds on the information complexity of augmented-index. In contrast to analogous results for index by Jain, Radhakrishnan, and Sen [J. ACM, 56 (2009), article 33], we have to overcome the significant technical challenge that protocols for augmented-index may violate the “rectangle property” due to the inherent input sharing. Second, we use these bounds to resolve an open problem of Magniez, Mathieu, and Nayak [Proceedings of the 42 nd Annual ACM Symposium on Theory of Computing, 2010, pp. 261--270] that asked about the multipass complexity of recognizing Dyck languages. This results in a natural separation between the standard multipass model and the multipass model that permits reverse passes. Third, we present the first passive memory checkers that verify the interaction transcripts of priority queues, stacks, and double-ended queues. We obtain tight upper and lower bounds for these problems, thereby addressing an important subclass of the memory checking framework of Blum et al. [Algorithmica, 12 (1994), pp. 225--244].

Mutual Exclusion is a fundamental problem in distributed computing, and the problem of proving upper and lower bounds on the RMR complexity of this problem has been extensively studied. Here, we give matching lower and upper bounds on how RMR complexity trades off with space. Two implications of our results are that constant RMR complexity is impossible with subpolynomial space and subpolynomial RMR complexity is impossible with constant space for cache-coherent multiprocessors, regardless of how strong the hardware synchronization operations are.

This paper makes three main contributions to the theory of communication complexity and stream computation. First, we present new bounds on the information complexity of AUGMENTED-INDEX. In contrast to analogous results for INDEX by Jain, Radhakrishnan and Sen [J. ACM, 2009], we have to overcome the significant technical challenge that protocols for AUGMENTED-INDEX may violate the "rectangle property" due to the inherent input sharing. Second, we use these bounds to resolve an open problem of Magniez, Mathieu and Nayak [STOC, 2010] on the multi-pass complexity of recognizing Dyck languages. This results in a natural separation between the standard multi-pass model and the multi-pass model that permits reverse passes. Third, we present the first passive memory checkers that verify the interaction transcripts of priority queues, stacks, and double-ended queues. We obtain tight upper and lower bounds for these problems, thereby addressing an important sub-class of the memory checking framework of Blum et al. [Algorithmica, 1994].