You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Interdependent Privacy 
Chapter Title:  Attacks. 
1. Abstract of Cited Paper (BIB001):  The amount of media uploaded to the Web is still rapidly expanding. The ease-of-use of modern smartphones in combination with the proliferation of high-speed mobile networks facilitates a culture of spontaneous and often carefree sharing of user-generated content, especially photos and videos. An increasing number of modern devices are capable of embedding location information and other metadata into created content. However, currently there is not much user awareness of possible privacy consequences of such data. While in most cases users upload their own media consciously, the flood of media uploaded by others is so huge that it is almost impossible for users to stay aware of all media that might be relevant to them. Current social network services and photo-sharing sites mainly focus on the privacy of users' own media in terms of access control, but offer few possibilities to deal with privacy implications created by other users' actions. We conducted an online survey with 414 participants. The results show that users would like to get more information about media shared by others. Based on an analysis of prevalent sharing services like Flickr, Facebook, or Google+ and an analysis of metadata of three different sets of crawled photos, we discuss privacy implications and potentials of the emerging trend of (geo-)tagged media. Finally, we present a novel concept on how location information can actually help users to control the flood of potentially infringing or interesting media. 
2. Abstract of Cited Paper (BIB002):  Social networks are often grounded in spatial locality where individuals form relationships with those they meet nearby. However, the location of individuals in online social networking platforms is often unknown. Prior approaches have tried to infer individuals' locations from the content they produce online or their online relations, but often are limited by the available location-related data. We propose a new method for social networks that accurately infers locations for nearly all of individuals by spatially propagating location assignments through the social network, using only a small number of initial locations. In five experiments, we demonstrate the effectiveness in multiple social networking platforms, using both precise and noisy data to start the inference, and present heuristics for improving performance. In one experiment, we demonstrate the ability to infer the locations of a group of users who generate over 74% of the daily Twitter message volume with an estimated median location error of 10km. Our results open the possibility of gathering large quantities of location-annotated data from social media platforms. 
3. Abstract of Cited Paper (BIB003):  This paper presents a concrete and widespread example of situation where a user’s location privacy is unintentionally compromised by others, specifically the location-privacy threat that exists at access points (public hotspots, FON, home routers, etc.) that have a single public IP and make use of network address translation (NAT). As users connected to the same hotspot share a unique public IP address, a single user’s making a location-based request is enough to enable a service provider to map the IP address of the hotspot to its geographic coordinates, thus compromising the location privacy of all the other connected users. When successful, the service provider can locate users within a few hundreds of meters, thus improving over existing IP-location databases. Even in the case where IPs change periodically (e.g., by using DHCP), the service provider is still able to update a previous (IP, Location) mapping by inferring IP changes from authenticated communications (e.g., cookies). The contribution of this paper is three-fold: (i) We identify a novel location-privacy threat caused by shared public IPs in combination with NAT. (ii) We formalize and analyze the threat theoretically. In particular we derive and provide expressions of the probability that the service provider will learn the mapping and of the expected proportion of victims. (iii) We experimentally assess the state in practice by using real traces (collected from deployed hotspots over a period of 23 days) of users who accessed Google services. We also discuss how existing countermeasures can thwart the threat. 
4. Abstract of Cited Paper (BIB004):  Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the names of the friends they are with. The users’ IP addresses also constitute a source of co-location information. Combined with (possibly obfuscated) location information, such co-locations can be used to improve the inference of the users’ locations, thus further threatening their location privacy: As co-location information is taken into account, not only a user's reported locations and mobility patterns can be used to localize her, but also those of her friends (and the friends of their friends and so on). In this paper, we study this problem by quantifying the effect of co-location information on location privacy, considering an adversary such as a social network operator that has access to such information. We formalize the problem and derive an optimal inference algorithm that incorporates such co-location information, yet at the cost of high complexity. We propose some approximate inference algorithms, including a solution that relies on the belief propagation algorithm executed on a general Bayesian network model, and we extensively evaluate their performance. Our experimental results show that, even in the case where the adversary considers co-locations of the targeted user with a single friend, the median location privacy of the user is decreased by up to 62 percent in a typical setting. We also study the effect of the different parameters (e.g., the settings of the location-privacy protection mechanisms) in different scenarios.

Literature Review:
Sadilek et al. tackle the problem of joint inference of friendship and location for social network users BIB001 ]. More specifically, the authors present an attack that infers friendships between users based on (among other things) co-location profiles and uses the inferred friendship information to later infer the location of a user based on that of their friends. The proposed attack relies on a simple temporal graphical model similar to a HMM (see Section 3.1), and it makes use of a variant of the forward-backward algorithm. They evaluate the attack on a dataset of N ≈ 1.2M Twitter users based in the U.S. and show that it can accurately infer the location of a user, based on that of their friends. Henne et al. study interdependent privacy risks, based on the content and meta-data of photos uploaded on online platforms such as OSNs BIB002 ] (discussed in Section 4.3). As the meta-data of photos can include both location and identity information, the risk described in this work falls in the category of location privacy as well. Jurgens studies location inference in OSN based on relationship between users, using a label propagation algorithm BIB003 ] that propagates location information about a user to their contacts. The authors collected data from Foursquare and Twitter to evaluate (cross-platform) inference based on prediction linguistic similarity, self-reported locations and also timezone boundaries (to filter out incorrect distant locations). Their results show that it is possible to infer the location of 50% of the users with an error of less than 10km. Vratonjic et al. identify an interdependent location-privacy risk, based on the use of shared public IP addresses  ]. As users connected to an access point that uses network address translation (NAT), a common technique for routers deployed in homes and public places, have the same public IP address, the fact that two individuals have the same IP address constitutes co-location data: It indicates that they are likely at the same location. If an adversary learns the location of a single individual connected to the access point, then it learns the location of the access point and those of all the other individuals connected to it (identified through their common public IP address that is known to the online service they use). The authors propose a theoretical probabilistic framework to quantify the risk, based on a number of characteristics of the Internet traffic of an access point. They also experimentally quantify the extent of the risk by using traffic logs from a university campus Wi-Fi network. In their follow-up work, Vratonjic et al. refine their theoretical model to account for the diurnal traffic pattern and demonstrate that it fits reality BIB004 ]. Olteanu et al. were the first to identify and to formalize the general problem of interdependent location-privacy  . They focus on the cases of posts and photos uploaded on OSNs and tagged with the names of multiple individuals. They extend the state-of-the-art theoretical framework to quantify location privacy by including observed co-location data. Their model is based on HMMs (see Section 3.1) and includes location and co-location reporting (in the presence of obfuscation and errors). They show that, with co-location data, the complexity of the inference process increases exponentially with the number of target individuals, and they propose a heuristic of reasonable complexity. They quantify the extent of the risk experimentally by relying on a dataset of location traces, that they enrich with synthetic co-location data. Their results show that co-location data substantially decreases the location privacy of individuals. In their follow-up work BIB005 , they refine their model by relying on Bayesian networks, which enables them to scale the inference process through belief propagation. They also take into account the case of erroneous co-location data.