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Although outsourcing data to cloud storage has become popular, the increasing concerns about data security and privacy in the cloud limits broader cloud adoption. Ensuring data security and privacy, therefore, is crucial for better and broader adoption of the cloud. This tutorial provides a comprehensive analysis of the state-of-the-art in the context of data security and privacy for outsourced data. We aim to cover common security and privacy threats for outsourced data, and relevant novel schemes and techniques with their design choices regarding security, privacy, functionality, and performance. Our explicit focus is on recent schemes from both the database and the cryptography and security communities that enable query processing over encrypted data and access oblivious cloud storage systems. 

Although outsourcing data to cloud storage has become popular, the increasing concerns about data security and privacy in the cloud blocks broader cloud adoption. Recent efforts have developed oblivious storage systems to hide both the data content and the data access patterns from an untrusted cloud provider. These systems have shown great progress in improving the efficiency of oblivious accesses. However, these systems mainly focus on privacy without considering fault-tolerance of different system components. This makes prior proposals impractical for cloud applications that require 24/7 availability. In this demonstration, we propose Pharos, the Privacy Hazards of Replicating ORAM Stores. We aim to highlight the data access pattern privacy hazards of naively applying common database replication and operation execution techniques such as locking and asymmetric quorums. 

Social media streams analysis can reveal the characteristics of people who engage with or write about different topics. Recent works show that it is possible to reveal sensitive attributes (e.g., location, gender, ethnicity, political views, etc.) of individuals by analyzing their social media streams. Although, the prediction of a user's sensitive attributes can be used to enhance the user experience in social media, revealing some attributes like the location could represent a threat on individuals. Users can obfuscate their location by posting about random topics linked to different locations. However, posting about random and sometimes contradictory topics that are not aligned with a user's online persona and posts could negatively affect the followers interested in her profile. This paper represents our vision about the future of user privacy on social media. Users can locally deploy a cyborg, an artificial intelligent system that helps people to defend their privacy on social media. We propose LocBorg, a location privacy preserving cyborg that protects users by obfuscating their location while maintaining their online persona. LocBorg analyzes the social media streams and recommends topics to write about that are similar to a user's topics of interest and aligned with the user's online persona but linked to other locations. 

Trending Topic Detection has been one of the most popular methods to summarize what happens in the real world through the analysis and summarization of social media content. However, as trending topic extraction algorithms become more sophisticated and report additional information like the characteristics of users that participate in a trend, significant and novel privacy issues arise. We introduce a statistical attack to infer sensitive attributes of Online Social Networks users that utilizes such reported community-aware trending topics. Additionally, we provide an algorithmic methodology that alters an existing community-aware trending topic algorithm so that it can preserve the privacy of the involved users while still reporting topics with a satisfactory level of utility. 

Towards the vision of building artificial intelligence systems that can assist with our everyday life, we introduce a proof of concept for a social media privacy "cyborg" which can locally and privately monitor a person's published content and offer advice or warnings when their privacy is at stake. The idea of a cyborg can be more general, as a separate local entity with its own computational resources, that can automatically perform several online tasks on our behalf. For this demonstration, we assume an attacker that can successfully infer user attributes, solely based on what the user has published (topic-based inference). We focus on Social Media privacy and specifically on the issue of exposing sensitive user-attributes, like location, or race, through published content. We built a privacy cyborg that can monitor a user's posted topics and automatically warn them in real time when a sensitive attribute is at risk of being exposed. 

A thorough understanding of social media discussions and the demographics of the users involved in these discussions has become critical for many applications like business or political analysis. Such an understanding and its ramifications on the real world can be enabled through the automatic summarization of Social Media. Trending topics are offered as a high level content recommendation system where users are suggested to view related content if they deem the displayed topics interesting. However, identifying the characteristics of the users focused on each topic can boost the importance even for topics that might not be popular or bursty. We define a way to characterize groups of users that are focused in such topics and propose an efficient and accurate algorithm to extract such communities. Through qualitative and quantitative experimentation we observe that topics with a strong community focus are interesting and more likely to catch the attention of users.