Since the beginning of the digital area, privacy and anonymity have been impacted drastically (both, positively and negatively), by the different technologies developed for communications purposes. The broad possibilities that the Internet offers since its conception, makes it a mandatory target for those entities that are aiming to know and control the different channels of communication and the information that flows through. In this paper, we address the current threats against privacy and anonymity on the Internet, together with the methods applied against them. In addition, we enumerate the publicly known entities behind those threats and their motivations. Finally, we analyze the state of the art concerning the protection of the privacy and anonymity on the Internet; introducing future lines of research.
As of today, there is still no single solution available that is complaint with the definitions of privacy and anonymity introduced in Section 1. Nowadays, every communication done through the Internet that aims to be private and anonymous requires the use of cryptography and hard-trace routing techniques. Proxies and TOR are among the most used anonymity technologies @cite_13 and public-key cryptography is the most popular method aiming for privacy. Nevertheless, it is still possible to perform different attacks over these techniques, leading always to some result that invalidate the required properties for privacy and anonymity mentioned in Section 1. We introduce some of the possible improvements that current lines of research are proposing in order to enforce better privacy and anonymity.
@cite_13
2021549941
Anonymity technologies enable Internet users to maintain a level of privacy that prevents the collection of identifying information such as the IP address. Understanding the deployment of anonymity technologies on the Internet is important to analyze the current and future trends. In this paper, we provide a tutorial survey and a measurement study to understand the anonymity technology usage on the Internet from multiple perspectives and platforms. First, we review currently utilized anonymity technologies and assess their usage levels. For this, we cover deployed contemporary anonymity technologies including proxy servers, remailers, JAP, I2P, and Tor with the geo-location of deployed servers. Among these systems, proxy servers, Tor and I2P are actively used, while remailers and JAP have minimal usage. Then, we analyze application-level protocol usage and anonymity technology usage with different applications. For this, we preform a measurement study by collecting data from a Tor exit node, a P2P client, a large campus network, a departmental email server, and publicly available data on spam sources to assess the utilization of anonymizer technologies from various perspectives. Our results confirm previous findings regarding application usage and server geo-location distribution where certain countries utilize anonymity networks significantly more than others. Moreover, our application analysis reveals that Tor and proxy servers are used more than other anonymity techniques. In this paper we study privacy preservation for the publication of search engine query logs. We introduce a new privacy concern, website privacy as a special case of business privacy.We define the possible adversaries who could be interested in disclosing website information and the vulnerabilities in the query log, which they could exploit. We elaborate on anonymization techniques to protect website information, discuss different types of attacks that an adversary could use and propose an anonymization strategy for one of these attacks. We then present a graph-based heuristic to validate the effectiveness of our anonymization method and perform an experimental evaluation of this approach. Our experimental results show that the query log can be appropriately anonymized against the specific attack, while retaining a significant volume of useful data. Limiting disclosure in data publishing requires a careful balance between privacy and utility. Information about individuals must not be revealed, but a dataset should still be useful for studying the characteristics of a population. Privacy requirements such as k-anonymity and l-diversity are designed to thwart attacks that attempt to identify individuals in the data and to discover their sensitive information. On the other hand, the utility of such data has not been well-studied.In this paper we will discuss the shortcomings of current heuristic approaches to measuring utility and we will introduce a formal approach to measuring utility. Armed with this utility metric, we will show how to inject additional information into k-anonymous and l-diverse tables. This information has an intuitive semantic meaning, it increases the utility beyond what is possible in the original k-anonymity and l-diversity frameworks, and it maintains the privacy guarantees of k-anonymity and l-diversity. Building on the popularity of online social networks (OSNs) such as Facebook, social content-sharing applications allow users to form communities around shared interests. Millions of users worldwide use them to share recommendations on everything from music and books to resources on the web. However, their increasing popularity is beginning to attract the attention of malicious attackers. As social network credentials become valued targets of phishing attacks and social worms, attackers look to leverage compromised accounts for further financial gain. In this paper, we analyze the state of privacy protection in social content-sharing applications, describe effective privacy attacks against today's social networks, and propose anonymization techniques to protect users. We show that simple protection mechanisms such as anonymizing shared data can still leave users open to social intersection attacks, where a small number of compromised users can identify the originators of shared content. Modeling this as a graph anonymization problem, we propose to provide users with k-anonymity privacy guarantees by augmenting the social graph with "latent edges." We identify StarClique, a locally minimal graph structure required for users to attain k-anonymity, where at worst, a user is identified as one of k possible contributors of a data object. We prove the correctness of our approach using analysis. Finally, using experiments driven by traces from the del.icio.us social bookmark site, we demonstrate the practicality and effectiveness of our approach on real-world systems.