Ransomware attacks have become widespread in the last few years and have affected many critical industries and infrastructures. Unfortunately, there are no recovery tools that can effectively defend against all types of ransomware. Approaches, such as frequent data backups, have several drawbacks. They are expensive in terms of resources and trained technical staff. Therefore, it is much more challenging and cost-consuming for average users and small business owners to survive ransomware attacks. To provide an easy-to-use tool for a broader population of users and businesses, we propose a novel ransomware defense mechanism that can be conveniently deployed in modern Windows systems which have over 76% market share as of 2022. The uniqueness of our approach is to fight malware like malware. We leverage Alternate Data Streams, which are sometimes used by malicious applications, to design and implement a data protection method that misleads the ransomware into attacking only file “shells” instead of the actual file content. We have evaluated our approach against different cryptographic ransomware. The results show that our approach is usable, efficient, and effective.

Most existing image privacy protection works focus mainly on the privacy of photo owners and their friends, but lack the consideration of other people who are in the background of the photos and the related location privacy issues. In fact, when a person is in the background of someone else’s photos, he/she may be unintentionally exposed to the public when the photo owner shares the photo online. Not only a single visited place could be exposed, attackers may also be able to piece together a person’s travel route from images. In this article, we propose a novel image privacy protection system, called LAMP, which aims to light up the location awareness for people during online image sharing. The LAMP system is based on a newly designed location-aware multi-party image access control model. Unlike previous works on small scales, the LAMP system is highly efficient and scalable as it can enforce privacy protection for billions of users on social networks in real time. The LAMP system automatically detects the user’s occurrences on photos regardless the user is the photo owner or not. Once a user is identified and the location of the photo is deemed sensitive according to the user’s privacy policy, the user’s face will be replaced with a synthetic face. A prototype of the system was implemented and evaluated to demonstrate its applicability in the real world.

People constantly share their photographs with others through various social media sites. With the aid of the privacy settings provided by social media sites, image owners can designate scope of sharing, e.g., close friends and acquaintances. However, even if the owner of a photograph carefully sets the privacy setting to exclude a given individual who is not supposed to see the photograph, the photograph may still eventually reach a wider audience, including those clearly undesired through unanticipated channels of disclosure, causing a privacy breach. Moreover, it is often the case that a given image involves multiple stakeholders who are also depicted in the photograph. Due to various personalities, it is even more challenging to reach agreement on the privacy settings for these multi-owner photographs. In this paper, we propose a privacy risk reminder system, called REMIND, which estimates the probability that a shared photograph may be seen by unwanted people-through the social graph-who are not included in the original sharing list. We tackle this problem from a novel angle by digging into the big data regarding image sharing history. Specifically, the social media providers possess a huge amount of image sharing information (e.g., what photographs are shared with whom) of their users. By analyzing and modeling such rich information, we build a sophisticated probability model that efficiently aggregates the image disclosure probabilities along different possible image propagation chains and loops. If the computed disclosure probability indicates high risks of privacy breach, a reminder is issued to the image owner to help revise the privacy settings (or, at least, inform the user about this accidental disclosure risk). The proposed REMIND system also has a nice feature of policy harmonization that helps resolve privacy differences in multi-owner photographs. We have carried out a user study to validate the rationale of our proposed solutions and also conducted experimental studies to evaluate the efficiency of the proposed REMIND system.

With the prevalence of online social networking, a large amount of studies have focused on online users' privacy. Existing work has heavily focused on preventing unauthorized access of one's personal information (e.g. locations, posts and photos). Very little research has been devoted into protecting the friend search engine, a service that allows people to explore others' friend lists. Although most friend search engines only disclose a partial view of one's friend list (e.g., k friends) or offer the ability to show all or no friends, attackers may leverage the combined knowledge from views obtained from different queries to gain a much larger social network of a targeted victim, potentially revealing sensitive information of a victim. In this paper, we propose a new friend search engine, namely FriendGuard, which guarantees the degree of friend exposure as set by users. If a user only allows k of his/her friends to be disclosed, our search engine will ensure that any attempts of discovering more friends of this user through querying the user's other friends will be a failure. The key idea underlying our search engine is the construction of a unique sub social network that is capable of satisfying query needs as well as controlling the degree of friend exposure. We have carried out an extensive experimental study and the results demonstrate both efficiency and effectiveness in our approach.