A Survey on Interdependent Privacy <s> Privacy <s> Edward J. Bloustein was the president of Rutgers University, and a distinguished scholar of the law. The four essays on privacy that comprise this book were completed over a thirteen-year period, and the development of the author's thinking parallels increasing thoughtful concern about privacy in the larger society. This development is especially appropriate to discussions of privacy and the "right to know" in the current era. The author analyzes individual and group privacy as legal concepts and examines the relationship of each to the legal right of the public to be informed about, and of a publisher to publish, private or confidential information. In exploring a series of problems associated with privacy and the First Amendment, Bloustein defines individual and group privacy, distinguishing them from each other and related concepts. He also identifies the public interest in individual privacy as individual integrity or liberty, and that of group privacy as the integrity of social structure. The legal protection afforded each of these forms of privacy is illustrated at length, as is the clash between them and the constitutional guarantees of the First Amendment and the citizen's general right to know. In his final essay, Bloustein insists that the concept of group privacy is essential to a properly functioning social structure, and warns that it would be disastrous if this principle were neglected as part of an overreaction to the misuse of group confidences that characterized the Nixon era. The new opening by Nathaniel Pallone provides a fresh context for evaluating the intellectual as well as organizational contribution of Bloustein. "A notable contribution to the emerging law of privacy Bloustein has not only developed a unified theory of privacy but he has gone far to relate it to the First Amendment right to freedom of expression and to other features of our constitutional structure."-Thomas Emerson, Yale Law School Edward J. Bloustein (1925-1989) was president of Rutgers, The State University of New Jersey from 1971 until his death in 1989, where he also served as professor of law and philosophy. Bloustein was also a professor of law at New York University. Nathaniel J. Pallone, who served in the Bloustein administration, is University Distinguished Professor (Psychology), Center for Alcohol Studies, at Rutgers-The State University of New Jersey. He is the author of "Criminal Behavior" as well as a number of other books in the area of criminology, many of them published by Transaction. <s> BIB001 </s> A Survey on Interdependent Privacy <s> Privacy <s> Social Networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of people participating. Social networks enable a form of self expression for users, and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social Network sites, Social Networks yet do not support any mechanism for collaborative management of privacy settings for shared content. In this paper, we model the problem of collaborative enforcement of privacy policies on shared data by using game theory. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness, and that rewards users who promote co-ownership. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge this is the first time such a protection mechanism for Social Networking has been proposed. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today's most popular social networks. We show that supporting these type of solutions is not also feasible, but can be implemented through a minimal increase in overhead to end-users. <s> BIB002 </s> A Survey on Interdependent Privacy <s> Privacy <s> Social networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of subscribed users. Social networks enable a form of self-expression for users and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social network sites, they do not provide any mechanisms for collective management of privacy settings for shared content. In this paper, using game theory, we model the problem of collective enforcement of privacy policies on shared data. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness and that rewards users who promote co-ownership. Our approach enables social network users to compose friendship based policies based on distances from an agreed upon central user selected using several social networks metrics. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge, this is the first time such a privacy protection mechanism for social networking has been proposed. We also extend our mechanism so as to support collective enforcement across multiple social network sites. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today's most popular social networks. Through our implementation, we show the feasibility of such approach and show that it can be implemented with a minimal increase in overhead to end-users. We complete our analysis by conducting a user study to investigate users' understanding of co-ownership, usefulness and understanding of our approach. Users responded favorably to the approach, indicating a general understanding of co-ownership and the auction, and found the approach to be both useful and fair. <s> BIB003 </s> A Survey on Interdependent Privacy <s> Privacy <s> As the popularity of social networks expands, the information users expose to the public has potentially dangerous implications for individual privacy. While social networks allow users to restrict access to their personal data, there is currently no mechanism to enforce privacy concerns over content uploaded by other users. As group photos and stories are shared by friends and family, personal privacy goes beyond the discretion of what a user uploads about himself and becomes an issue of what every network participant reveals. In this paper, we examine how the lack of joint privacy controls over content can inadvertently reveal sensitive information about a user including preferences, relationships, conversations, and photos. Specifically, we analyze Facebook to identify scenarios where conflicting privacy settings between friends will reveal information that at least one user intended remain private. By aggregating the information exposed in this manner, we demonstrate how a user's private attributes can be inferred from simply being listed as a friend or mentioned in a story. To mitigate this threat, we show how Facebook's privacy model can be adapted to enforce multi-party privacy. We present a proof of concept application built into Facebook that automatically ensures mutually acceptable privacy restrictions are enforced on group content. <s> BIB004 </s> A Survey on Interdependent Privacy <s> Privacy <s> Online Social Networks (OSNs) facilitate the creation and maintenance of interpersonal online relationships. Unfortunately, the availability of personal data on social networks may unwittingly expose users to numerous privacy risks. As a result, establishing effective methods to control personal data and maintain privacy within these OSNs have become increasingly important. This research extends the current access control mechanisms employed by OSNs to protect private information shared among users of OSNs. The proposed approach presents a system of collaborative content management that relies on an extended notion of a “content stakeholder.” A tool, Collaborative Privacy Management (CoPE), is implemented as an application within a popular social-networking site, , to ensure the protection of shared images generated by users. We present a user study of our CoPE tool through a survey-based study (n=80). The results demonstrate that regardless of whether Facebook users are worried about their privacy, they like the idea of collaborative privacy management and believe that a tool such as CoPE would be useful to manage their personal information shared within a social network. © 2011 Wiley Periodicals, Inc. <s> BIB005 </s> A Survey on Interdependent Privacy <s> Privacy <s> Online social networks (OSNs) have experienced tremendous growth in recent years and become a de facto portal for hundreds of millions of Internet users. These OSNs offer attractive means for digital social interactions and information sharing, but also raise a number of security and privacy issues. While OSNs allow users to restrict access to shared data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users. To this end, we propose an approach to enable the protection of shared data associated with multiple users in OSNs. We formulate an access control model to capture the essence of multiparty authorization requirements, along with a multiparty policy specification scheme and a policy enforcement mechanism. Besides, we present a logical representation of our access control model that allows us to leverage the features of existing logic solvers to perform various analysis tasks on our model. We also discuss a proof-of-concept prototype of our approach as part of an application in Facebook and provide usability study and system evaluation of our method. <s> BIB006 </s> A Survey on Interdependent Privacy <s> Privacy <s> Users share massive amounts of personal information and opinion with each other and different service providers every day. In such an interconnected setting, the privacy of individual users is bound to be affected by the decisions of others, giving rise to the phenomenon which we term as interdependent privacy. In this paper we define online privacy interdependence, show its existence through a study of Facebook application permissions, and model its impact through an Interdependent Privacy Game (IPG). We show that the arising negative externalities can steer the system into equilibria which are inefficient for both users and platform vendor. We also discuss how the underlying incentive misalignment, the absence of risk signals and low user awareness contribute to unfavorable outcomes. <s> BIB007 </s> A Survey on Interdependent Privacy <s> Privacy <s> Online Social Networks (OSNs) such as Facebook, Twitter, and so on recently are major impact in communication and social interaction. Users can share any information with others. However, they have concerns about losing privacy due to lack of an adequate privacy protection provided by the OSNs. The information posted by the user (owner) might leak to unwanted target users. Especially, when collaborative information (e.g. text, photo, video, link), which has associated with the owner and multiple users (co-owners) in the real world, is posted into the OSNs, the co-owners do not have permission to control and might not be aware their information that is being managed by others. To overcome, collective privacy protection (CPP) is proposed to balance between the collaborative information sharing and the privacy protection for the owner and co-owners by majority vote. It enables the owner to create the privacy policy and the co-owners to make a decision in the privacy policy by vote. It additionally identifies and solves the privacy conflicts because at least one co-owner intends to keep private. <s> BIB008 </s> A Survey on Interdependent Privacy <s> Privacy <s> The lack of multi-party privacy management support in current mainstream Social Media infrastructures makes users unable to appropriately control to whom co-owned items are shared. Computational mechanisms that are able to merge the privacy preferences of multiple users into a single policy for these kind of items can help solve this problem. As privacy preferences may conflict, these mechanisms need to consider how users' would actually reach an agreement in order to propose acceptable solutions to the conflicts. We propose the first computational mechanism to resolve conflicts for multi-party privacy management in Social Media that adapts to different situations that may motivate different users' concessions and agreements. <s> BIB009 </s> A Survey on Interdependent Privacy <s> Privacy <s> While much attention is given to young people’s online privacy practices on sites like Facebook, current theories of privacy fail to account for the ways in which social media alter practices of information-sharing and visibility. Traditional models of privacy are individualistic, but the realities of privacy reflect the location of individuals in contexts and networks. The affordances of social technologies, which enable people to share information about others, further preclude individual control over privacy. Despite this, social media technologies primarily follow technical models of privacy that presume individual information control. We argue that the dynamics of sites like Facebook have forced teens to alter their conceptions of privacy to account for the networked nature of social media. Drawing on their practices and experiences, we offer a model of networked privacy to explain how privacy is achieved in networked publics. <s> BIB010 </s> A Survey on Interdependent Privacy <s> Privacy <s> The popularity of third-party apps on social network sites and mobile networks emphasizes the problem of the interdependency of privacy. It is caused by users installing apps that often collect and potentially misuse the personal information of users’ friends who are typically not involved in the decision-making process. In this paper, we provide an economic model and simulation results addressing this problem space. We study the adoption of social apps in a network where privacy consequences are interdependent. Motivated by research in behavioral economics, we extend the model to account for users’ other-regarding preferences; that is, users care about privacy harms they inflict on their peers. <s> BIB011 </s> A Survey on Interdependent Privacy <s> Privacy <s> The evolution of mobile devices, the success of social networks, and the digitalization of business/personal services have resulted in a huge and continuous production of Personal Data (PD). The creation of a balanced ecosystem of PD, where data act as the fuel for novel application scenarios, may drive the shift toward a user-centric paradigm, in which constraints should be imposed on the data usage, to protect the individuals’ privacy. The possibility for people to directly collect, manage and exploit PD introduces both technical and regulatory new issues in PD management. Uncertainty especially arises in the case of PD related to multiple subjects, e.g., containing identifiers referring to more than one person, each of which holds rights to control how these PD are treated. In this paper, we refer to this kind of valuable data as Multiple Subjects Personal Data (MSPD). The protection of MSPD in a user-centric paradigm is an undeniable requirement to ensure privacy to all MSPD right-holders. We discuss the relevance of MSPD, providing a technical approach to regulate their trusted management in a user-centric model context. <s> BIB012 </s> A Survey on Interdependent Privacy <s> Privacy <s> Social network services enable users to share large quantities of private information. Often, the shared information concerns individuals who are members of the social network but did not upload the information to the service. In such situations, inappropriate sharing preferences can cause conflict and threaten users’ privacy. Since related studies suggest that users prefer to solve multi-party privacy conflicts through negotiation, we introduce a novel approach based on negotiation through arguments. In our approach, users propose privacy settings and support their proposals with logical arguments. The final decision is based on a setting supported by sound arguments. <s> BIB013 </s> A Survey on Interdependent Privacy <s> Privacy <s> Building on recent work in privacy management and disclosure in networked spaces, this two-day workshop examines networked privacy challenges from a broader perspective by (1) identifying the most important issues researchers will need to address in the next decade and (2) working to create actionable solutions for these privacy issues. This workshop comes at a critical time for organizations, researchers, and consumers, as content-sharing applications soar in popularity and more privacy and security vulnerabilities emerge. Workshop participants and organizers will work together to develop a guiding framework for the community that highlights the future challenges and opportunities of networked privacy. <s> BIB014 </s> A Survey on Interdependent Privacy <s> Privacy <s> In online social networks, because one's private information can be co-owned and disclosed by peers, a new type of privacy concern, i.e., information privacy concern about peer disclosure (IPCPD), looms large. Should a member of an online social network be offered decision control to veto ex-ante the disclosure of his/her information by peers? There has been no theoretical research on the effectiveness of decisional control to alleviate such a privacy concern. Drawing on the communication privacy management perspective and the impression management theory, this study proposes a three-way interaction effect among three important antecedents of IPCPD: 1) decisional control; 2) image discrepancy (i.e., the degree to which the disclosed information portrays an unfavorable image of the disclosed person); and 3) social network overlap between the disclosed person and the discloser. Our experimental study reveals significant findings. When image discrepancy is low, decisional control is helpful only when the social network overlap between the disclosed one and the discloser is low. When image discrepancy is high, decisional control is helpful only when the social network overlap between the disclosed one and the discloser is high. This study contributes to a theory of privacy concern about peer disclosure in the context of online social networks. <s> BIB015 </s> A Survey on Interdependent Privacy <s> Privacy <s> Users of social network services (SNSs) have to cope with a new set of privacy challenges because personal information on an SNS is often co-owned and co-managed by various distributed social ties. Using a multi-methods and multinational approach, we investigated Facebook users' privacy behavior by focusing on how they co-manage private information. Our findings from focus-group interviews (n = 28) and online surveys (n = 299) suggest that Facebook users primarily apply four different practices of privacy management: collaborative strategies, corrective strategies, preventive strategies, and information control. The four dimensions of privacy management display selective relationships with theoretical antecedents (e.g., self-efficacy, collective-efficacy, attitudes, privacy concern), indicating that each behavior is motivated by a different combination of conditions. Implications for research and practice are discussed. <s> BIB016 </s> A Survey on Interdependent Privacy <s> Privacy <s> Third-party apps enable a personalized experience on social networking platforms; however, they give rise to privacy interdependence issues. Apps installed by a user’s friends can collect and potentially misuse her personal data inflicting collateral damage on the user while leaving her without proper means of control. In this paper, we present a multi-faceted study on the collateral information collection of apps in social networks. We conduct a user survey and show that Facebook users are concerned about this issue and the lack of mechanisms to control it. Based on real data, we compute the likelihood of collateral information collection affecting users; we show that the probability is significant and depends on both the friendship network and the popularity of the app. We also show its significance by computing the proportion of exposed user attributes including the case of profiling, when several apps are offered by the same provider. Finally, we propose a privacy dashboard concept enabling users to control the collateral damage. <s> BIB017 </s> A Survey on Interdependent Privacy <s> Privacy <s> According to the current design of content sharing services, such as Online Social Networks (OSNs), typically (i) the service provider has unrestricted access to the uploaded resources and (ii) only the user uploading the resource is allowed to define access control permissions over it. This results in a lack of control from other users that are associated, in some way, with that resource. To cope with these issues, in this paper, we propose a privacy-preserving system that allows users to upload their resources encrypted, and we design a collaborative multi-party access control model allowing all the users related to a resource to participate in the specification of the access control policy. Our model employs a threshold-based secret sharing scheme, and by exploiting users' social relationships, sets the trusted friends of the associated users responsible to partially enforce the collective policy. Through replication of the secret shares and delegation of the access control enforcement role, our model ensures that resources are timely available when requested. Finally, our experiments demonstrate that the performance overhead of our model is minimal and that it does not significantly affect user experience. <s> BIB018 </s> A Survey on Interdependent Privacy <s> Privacy <s> Current collaborative photo privacy protection solutions can be categorized into two approaches: controlling the recipient, which restricts certain viewers' access to the photo, and controlling the content, which protects all or part of the photo from being viewed. Focusing on the latter approach, we introduce privacy-enhancing obfuscations for photos and conduct an online experiment with 271 participants to evaluate their effectiveness against human recognition and how they affect the viewing experience. Results indicate the two most common obfuscations, blurring and pixelating, are ineffective. On the other hand, inpainting, which removes an object or person entirely, and avatar, which replaces content with a graphical representation are effective. From a viewer experience perspective, blurring, pixelating, inpainting, and avatar are preferable. Based on these results, we suggest inpainting and avatar may be useful as privacy-enhancing technologies for photos, because they are both effective at increasing privacy for elements of a photo and provide a good viewer experience. <s> BIB019 </s> A Survey on Interdependent Privacy <s> Privacy <s> Cloud storage services, like Dropbox and Google Drive, have growing ecosystems of 3rd party apps that are designed to work with users' cloud files. Such apps often request full access to users' files, including files shared with collaborators. Hence, whenever a user grants access to a new vendor, she is inflicting a privacy loss on herself and on her collaborators too. Based on analyzing a real dataset of 183 Google Drive users and 131 third party apps, we discover that collaborators inflict a privacy loss which is at least 39% higher than what users themselves cause. We take a step toward minimizing this loss by introducing the concept of History- based decisions. Simply put, users are informed at decision time about the vendors which have been previously granted access to their data. Thus, they can reduce their privacy loss by not installing apps from new vendors whenever possible. Next, we realize this concept by introducing a new privacy indicator, which can be integrated within the cloud apps' authorization interface. Via a web experiment with 141 participants recruited from CrowdFlower, we show that our privacy indicator can significantly increase the user's likelihood of choosing the app that minimizes her privacy loss. Finally, we explore the network effect of History-based decisions via a simulation on top of large collaboration networks. We demonstrate that adopting such a decision- making process is capable of reducing the growth of user privacy loss by 40% in a Google Drive-based network and by 70% in an author collaboration network. This is despite the fact that we neither assume that users cooperate nor that they exhibit altruistic behavior. To our knowledge, our work is the first to provide quantifiable evidence of the privacy risk that collaborators pose in cloud apps. We are also the first to mitigate this problem via a usable privacy approach. <s> BIB020 </s> A Survey on Interdependent Privacy <s> Privacy <s> 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. <s> BIB021 </s> A Survey on Interdependent Privacy <s> Privacy <s> Individuals share increasing amounts of personal data online. This data often involves–or at least has privacy implications for–data subjects other than the individuals who shares it (e.g., photos, genomic data) and the data is shared without their consent. A sadly popular example, with dramatic consequences, is revenge pornography. In this paper, we propose ConsenShare, a system for sharing, in a consensual (wrt the data subjects) and privacy-preserving (wrt both service providers and other individuals) way, data involving subjects other than the uploader. We describe a complete design and implementation of ConsenShare for photos, which relies on image processing and cryptographic techniques, as well as on a two-tier architecture (one entity for detecting the data subjects and contacting them; one entity for hosting the data and for collecting consent). We benchmark the performance (CPU and bandwidth) of ConsenShare by using a dataset of 20k photos from Flickr. We also conduct a survey targeted at Facebook users (N = 321). Our results are quite encouraging: The experimental results demonstrate the feasibility of our approach (i.e., acceptable overheads) and the survey results demonstrate a potential interest from the users. <s> BIB022 </s> A Survey on Interdependent Privacy <s> Privacy <s> From the"right to be left alone"to the"right to selective disclosure", privacy has long been thought as the control individuals have over the information they share and reveal about themselves. However, in a world that is more connected than ever, the choices of the people we interact with increasingly affect our privacy. This forces us to rethink our definition of privacy. We here formalize and study, as local and global node- and edge-observability, Bloustein's concept of group privacy. We prove edge-observability to be independent of the graph structure, while node-observability depends only on the degree distribution of the graph. We show on synthetic datasets that, for attacks spanning several hops such as those implemented by social networks and current US laws, the presence of hubs increases node-observability while a high clustering coefficient decreases it, at fixed density. We then study the edge-observability of a large real-world mobile phone dataset over a month and show that, even under the restricted two-hops rule, compromising as little as 1% of the nodes leads to observing up to 46% of all communications in the network. More worrisome, we also show that on average 36\% of each person's communications would be locally edge-observable under the same rule. Finally, we use real sensing data to show how people living in cities are vulnerable to distributed node-observability attacks. Using a smartphone app to compromise 1\% of the population, an attacker could monitor the location of more than half of London's population. Taken together, our results show that the current individual-centric approach to privacy and data protection does not encompass the realities of modern life. This makes us---as a society---vulnerable to large-scale surveillance attacks which we need to develop protections against. <s> BIB023 </s> A Survey on Interdependent Privacy <s> Privacy <s> Most popular location-based social networks, such as Facebook and Foursquare, let their users post location and co-location information (with other users). Such posts bring social benefits to the users who post them but also to their friends, who view them. Yet, they represent a severe threat to the users’ (location) privacy, as co-location information introduces interdependence between users. In this paper, we propose a game theoretical framework for analyzing and predicting the strategic behaviours of users of social networks regarding (co)-location information sharing. In addition, in order to design parametric utility functions as representative as possible of the users’ actual preferences, we conduct a survey targeted at Facebook users and rely on conjoint analysis for quantifying the users benefits of sharing vs. viewing (co)-location information and their preference for privacy vs. benefits.We evaluate our framework through data-driven numerical simulations. We show how users’ individual preferences influence each others’ decisions and identify several factors that significantly impact these decisions, specifically the considered adversary and the preference for privacy vs. benefits. <s> BIB024 </s> A Survey on Interdependent Privacy <s> Privacy <s> Genetics and genetic data have been the subject of recent scholarly work, with significant attention paid towards understanding consent practices for the acquisition and usage of genetic data as well as genetic data security. Attitudes and perceptions concerning the trustworthiness of governmental institutions receiving test-taker data have been explored, with varied findings, but no robust models or deterministic relationships have been established that account for these differences. These results also do not explore in detail the perceptions regarding other types of organizations (e.g., private corporations). Further, considerations of privacy interdependence arising from blood relative relationships have been absent from the conversation regarding the sharing of genetic data. This paper reports the results from a factorial vignette survey study in which we investigate how variables of ethnicity, age, genetic markers, and association of data with the individual's name affect the likelihood of sharing data with different types of organizations. We also investigate elements of personal and interdependent privacy concerns. We document the significant role these factors have in the decision to share or not share genetic data. We support our findings with a series of regression analyses. <s> BIB025
Westin defines privacy as the right to control, edit, manage, and delete information about oneself and to decide when, how, and to what extent information is communicated to others  . Interestingly, in this definition, the word "information" is used (and not data); this means that this definition applies to any data that reveals information about the considered individual. According to this definition, every individual should be able to manage and protect information about themselves, individually and independently from the others. This is unfortunately not always possible, either because an individual does not have the full control over the sharing policies (i.e., to which audience the data is revealed) of the data or because of the intrinsic properties of BIB002 ] BIB018 Xu 2016a, 2016b; BIB008 BIB003 BIB005 ] multi-party privacy BIB004 ] BIB013 BIB006 BIB019 BIB009 Such and Rovatsos 2016] networked privacy  ] BIB016 BIB010 BIB014 ] interdependent privacy BIB007 BIB020 BIB021 BIB022 BIB024 BIB011 BIB017 BIB025 ] peer privacy BIB015 [Ozdemir et al. 2017] group privacy BIB001 ] BIB023 ] multiple-subject privacy BIB012 [ BIB022 the data (i.e., correlation between the data of different individuals or data revealing information about multiple individuals). Nissenbaum defines privacy as contextual integrity, where each context is associated with social norms  ]. In her definition, the five critical parameters that define contextual informational norms are the data subject, the sender of the data, the recipient of the data, the information type, and the transmission principle. As shown in this survey, among these parameters, the data subject, the sender of the data, and the recipient of the data could be subject to interdependent privacy issues, which raises new challenges to be addressed. The case where the actions of some individuals affect the privacy of others has been increasingly studied over the past decade, by different communities using different terminologies-in isolation as we will see. Hereafter, we list the definitions used in the literature of these communities. We summarize the results in Table 1 . In this article, including its title, we use the term interdependent privacy by analogy with the general concept of interdependent security and because we believe it describes best the considered situations.