With the recent surge of social networks like Facebook, new forms of recommendations have become possible – personalized recommendations of ads, content, and even new friend and product connections based on one’s social interactions. Since recommendations may use sensitive social information, it is speculated that these recommendations are associated with privacy risks. The main contribution of this work is in formalizing these expected trade-offs between the accuracy and privacy of personalized social recommendations. In this paper, we study whether“social recommendations”, or recommendations that are solely based on a user’s social network, can be made without disclosing sensitive links in the social graph. More precisely, we quantify the loss in utility when existing recommendation algorithms are modified to satisfy a strong notion of privacy, called differential privacy. We prove lower bounds on the minimum loss in utility for any recommendation algorithm that is differentially private. We adapt two privacy preserving algorithms from the differential privacy literature to the problem of social recommendations, and analyze their performance in comparison to the lower bounds, both analytically and experimentally. We show that good private social recommendations are feasible only for a small subset of the users in the social network or for a lenient setting of privacy parameters. 1.

Abstract. We are witnessing a widespread of web syndication technologies such as RSS or Atom for a timely delivery of frequently updated Web content. Almost every personal weblog, news portal, or discussion forum employs nowadays RSS/Atom feeds for enhancing pull-oriented searching and browsing of web pages with push-oriented protocols of web content. Social media applications such as Twitter or Facebook also employ RSS for notifying users about the newly available posts of their preferred friends. Unfortunately, previous works on RSS/Atom statistical characteristics do not provide a precise and updated characterization of feeds ’ behavior and content, characterization which can be used to successfully benchmark effectiveness and efficiency of various RSS processing/analysis techniques. In this paper, we present the first thorough analysis of three complementary features of real-scale RSS feeds, namely, publication activity, items structure and length, as well as, vocabulary of its content which we believe are crucial for Web 2.0 applications. Keywords: RSS/Atom Feeds, Publication activity, Items structure and length, textual vocabulary composition and evolution 1

NTNU, Trondheim, with Bjørn Olstad as main supervisor, and Øystein Torbjørnsen and Magnus Lie Hetland as co-supervisors. The candidate was supported by the Research Council of Norway under the grant NFR 162349, and by the iAD project, also funded by the Research Council of Norway. 5 Summary This PhD thesis is a collection of papers presented with a general introduction to the topic, which is twig pattern matching (TPM) on indexed tree data. TPM is a pattern matching problem where occurrences of a query tree are found in a usually much larger data tree. This has applications in XML search, where the data is tree shaped and the queries specify tree patterns. The papers included present contributions on how to construct and use structure indexes, which can speed up pattern matching, and on how to efficiently join together results for the different parts of the query with so-called twig joins. • Paper 1 [18] shows how to perform more efficient matching of root-to-leaf query paths in so-called path indexes, by using new opportunistic algorithms on existing

Real-time search dictates that new contents be made available for search immediately following their creation. From the database perspective, this requirement may be quite easily met by creating an up-to-date index for the contents and measuring search quality by the time gap between insertion time and availability of the index. This approach, however, poses new challenges for micro-blogging systems where thousands of concurrent users may upload their micro-blogs or tweets simultaneously. Due to the high update and query loads, conventional approaches would either fail to index the huge amount of newly created contents in real time or fall short of providing a scalable indexing service. In this paper, we propose a tweet index called the TI (Tweet Index), an adaptive indexing scheme for microblogging systems such as Twitter. The intuition of the TI is to index the tweets that may appear as a search result with high probability and delay indexing some other tweets. This strategy significantly reduces the indexing cost without compromising the quality of the search results. In the TI, we also devise a new ranking scheme by combining the relationship between the users and tweets. We group tweets into topics and update the ranking of a topic dynamically. The experiments on a real Twitter dataset confirm the efficiency of the TI.

NTNU, Trondheim, with Bjørn Olstad as main supervisor, and Øystein Torbjørnsen and Magnus Lie Hetland as co-supervisors. The candidate was supported by the Research Council of Norway under the grant NFR 162349, and by the iAD project, also funded by the Research Council of Norway. 5 Summary This PhD thesis is a collection of papers presented with a general introduction to the topic, which is twig pattern matching (TPM) on indexed tree data. TPM is a pattern matching problem where occurrences of a query tree are found in a usually much larger data tree. This has applications in XML search, where the data is tree shaped and the queries specify tree patterns. The papers included present contributions on how to construct and use structure indexes, which can speed up pattern matching, and on how to efficiently join together results for the different parts of the query with so-called twig joins. • Paper 1 [18] shows how to perform more efficient matching of root-to-leaf query paths in so-called path indexes, by using new opportunistic algorithms on existing

More and more data is accumulated inside social networks. Keywordsearch providesasimple interface for exploringthis content. However, a lot of the content is private, and a search system must enforce the privacy settings of the social network. In this paper, we present a workload-aware keyword search system with access control based on a social network. We make two technical contributions: (1) HeapUnion, a novel union operator that improves processing of search queries with access control by up to a factor of two compared to the best previous solution; and (2) highly accurate cost models that vary in sophistication and accuracy; these cost models provide input to an optimization algorithm that selects the most efficient organization of access control meta-data for a given workload. Our experimental results with real and synthetic data show that our approach outperforms previous work by up to a factor of three.

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