Abstract
This paper discusses the issue of privacy in social networks with respect to advances in machine learning. It shows how machine learning protocols have been developed both to enhance and secure privacy as well as to invade privacy and collect, analyze, predict data based on users information and experience online. The conflict between these two directions in machine learning is likely to lead to a system wherein machine learning algorithms are actively engaged in the subversion of one another, with one attempting to conceal data and the other attempting to uncover it. This paper concludes with recommendations for social networks and the issue of privacy regarding machine learning.
Introduction
Social networks have allowed an ocean of personal data to form that is now sitting there waiting for machine learning algorithms to collect it, analyze it, and recognize individuals on social media (Oh, Benenson, Fritz & Schiele, 2016). Machine learning algorithms are thus being used more and more in social networks to collect data on users and to assess their browsing and personal informationand in doing so they could soon be predicting someones recreational activities or political affiliation through a simple analysis of an individuals social media use, such as posts on Twitter or the friends one has on Facebook (Lindsey, 2019). As a result, the privacy of individual social media users may be in jeopardy. This paper will review the findings of the related literature on this subject and discuss them and the recommendations for addressing this issue in the future.
Review of Literature
Privacy and information sharing may seem like two diametrically opposed concepts in the context of social media, and to a high degree they are. Mobile devices allow users to set information sharing settings that allow algorithms on other applications to identify a persons location, habits, and view other information to personalize ads and so on. The information that is available for viewing by machine learning programs is enormous, and many users do not even realize it. Machine learning algorithms often know more about a users habits and choices than the user does. Bilogrevic et al. (2016) point out that by analyzing peoples sharing behaviors in different contexts, it is shown in these works that it is possible to determine the features that most influence users sharing decisions, such as the identity of the person that is requesting the information and the current location (p. 126). The problem is that users do not know how to articulate their own personal information settings desires, nor are these desires static. For that reasons Bilogrevic et al. (2016) created a program that uses machine learning AI to automatically set those settings based on user interaction on the Web, reducing the users worry about when it is appropriate to share information and when it is not. The program developed by Bilogrevic et al. (2016) will do it for them.
Such a program is one example of the way privacy concerns and machine learning advancements are meeting in the realm of social media. One reason it is needed is, as Oh et al. (2016) show, there is no such thing as privacy on the Internet, and AI is being developed to collect as much data on what is out there as possible. The implications for ones privacy are enormous, especially as more and more people put their entire lives online (Oh et al., 2016). Yet while there are machine learning programs being developed to collect information on users in order to recognize them, create profiles of them, and predict their behaviors, there are machine learning programs like the one developed by Bilogrevic (2016) that are simultaneously being designed to protect and preserve users privacy.
Another such example is in the study by Mohassel and Zhang (2017). Mohassel and Zhang (2017) use a two-server model with machine learning algorithms for the purposes of training linear regression, logistic regression and neural networks models: in this model, in a setup phase, the data owners (clients) process, encrypt and/or secret-share their data among two non-colluding servers. In the computation phase, the two servers can train various models on the clients joint data without learning any information beyond the trained model (p. 19). Their protocol as a result is 1100- 1300 faster that previous protocols developed by engineers to protect end users privacy in an Internet environment where scanning algorithms and constantly looking for data on users to develop their own profiles. Their protocol acts as a wall between the user and the other machine learning AI seeking their information.
Bonawitz et al. (2017) look at some of the reasons machine learning models have become of so much interest in recent years. They note that these models have practical applications that can be used to enhance the public good: for instance, they can facilitate everything from medical screening to disease outbreak discovery (Bonawitz et al., 2017, p. 1175). This is important to remember because in the debate over whether these machine learning models are good or bad, have positive or negative ramifications for society, their potential uses will be part of that discussion and any ignorance of the possible benefits of their application will be judged as bias. Thus, there is a need to balance the positive returns that this technology could produce against the potential harms they could lead to. Somewhere within that discussion is the issue of privacy, but how it fits into the nexus of technology for safety vs. technology to preserve autonomy and privacy is still unclear and up for debate. Both sides of the issue have their points, and there will be considerable back-and-forth before they are resolvedif they ever are, that is.
One case in point regarding the importance of making sure that data collection instruments are not abused or used recklessly is provided by Bonawitz et al. (2017), who describe the case of a nominee for the US Supreme Court, whose video rental history was mishandled and released in 1988 without his consent and scuttling his reputation in the process. The fallout from that technical debacle was a new law from legislators seeking to prevent a similar mishandling of private data and the law passed in response to that incident remains relevant today, limiting how online video streaming services can use their user data (Bonawitz et al., 2017, p. 1175). However, laws are one thing and working technology is quite another, and that is where the protocol from Bonawitz et al. (2017) comes into play. The technology that they present is a protocol that securely computes sums of various vectors, for a constant number of rounds, with low communication overhead and robustness to failures, and which uses only one server with limited trust (Bonawitz et al., 2017, p. 1176). The outcome is that protection is provided against honest-but-curious (passive) adversaries.
Hunt, Song, Shokri, Shmatikov and Witchel (2018) designed a system that provides privacy-preserving machine learning as a service to clients who are storing data on the cloud. Their protocol named Chiron preserves privacy by hiding training data from the service operator and also hiding the algorithm and structure of the model from the end user, in effect leaving only a black-box access to the trained model (Hunt et al., 2018, p. 1). Their method for using machine learning to provide this service is based on the Ryoan sandbox designed to prevent data leaks: to enforce data confidentiality while allowing the provider to select, configure, and train a model any way they want, Chiron employs a Ryoan sandbox, which in turn is based on a hardware-protected enclave such as Intels SGX (Hunt et al., 2018, p. 1-2). The model works but the problem of advances by adversaries remains an issue.
Mooney and Pejaver (2018) look at the ethical implications of machine learning playing an active role in collection and analysis of Big Data and what it means for the privacy rights of health care patients. Their argument is that not every bit of data should be made available to machine learning algorithms and that these algorithms represent a serious risk to patients privacy rights and could incur HIPAA violations. Thus, there is a need to moderate and control their influence and interference.
So, Guler, Avestimehr and Mohassel (2019) discuss the issue of training a machine learning model while making sure the users data remains private and secure. Their solution is CodedPrivateML, which keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed workers (p. 1). Similar to what Hunt et al. (2018) did with Chiron, So et al. (2019) do with CodedPrivateML. The similarity of approach to machine learning training models and the need to maintain privacy shows that this issue is far from being resolved.
Balle et al. (2019) likewise focus on the problem of machine learning and privacy rights. They discuss the merits of the various approaches in the realm of cryptography, security and machine learning with a view to identifying which methods are most efficient for preserving privacy via machine learning. While helpful, the overall discussion illustrates why the issue is unlikely to be resolved anytime soon: as quickly as solutions and protections can be developed, they can be overcome because machine learning advances are being made on both sides. It is similar to a situation like the Cold War where weapons stockpiling occurs by the two main powers because neither can stop lest a vulnerability be perceived. At some point there will be massive gap between the protected and the highly vulnerable who have not kept pace with these advances.
Xu, Yue, Guo, Guo and Fang (2015) look at the problem of how to share big data among distributed data processing entities while mitigating privacy concerns (p. 318). Their focus is on the preservation of privacy while at the same time being able to share the necessary information in a network. The system they recommend is one where it can fit within the Data Parallel Systems, which are needed for Big Data sharing.
As they note, with a system such as Google File System(GFS) and MapReduce, clusters can be created using commodity hardware so that each node assumes the responsibility of providing computational and storage duties, which is essentially the concept of data locality. The system can be useful as local Mappers can work independently to get local training results, which are then summarized by a secure protocol on Reduce (Xu et al, 2015, p. 326).
Findings and Recommendations
What the findings show are that the need for privacy protection is at the forefront of the machine learning revolution, and researchers have developed multiple ways of addressing privacy concerns with regard to machine learning, whether through the use of Reduce, cryptography, CodedPrivateML, Chiron or some other protocol. The problem is that even with these developments, machine learning algorithms are being developed and used on the other sideto probe, predict, gather and interpret data on users for a variety of reasons, some beneficial and some harmful. At any rate, the risk to privacy protection is significant and there does not appear to be a point at which the problem is likely ever to be solved once and for all. The Internet has opened up a world of coding that in a state of constant revolution.
The recommendations based on these findings are for legislation to be developed and passed that will address the issue of using machine learning to violate privacy rights on social networks. Just as legislation emerged following the embarrassment of a U.S. Supreme Court Justice nominee in the 1980s, todays legislators need to understand the parameters and risks of machine learning with respect to data privacy on social networks. AI can be used to protect and to violate privacy rights, and laws should be in place that dictate what is acceptable behavior on the part of machine learning developers and what is not. Until that time comes, the contest between advancing machine learning protocols to protect or to collect will only escalate.
Social networks need to be protected as personal data is left behind or made available by users, who often do not even know how to navigate the web in terms of sharing information or making data visible to machine learning algorithms. End users are some of the least informed or least prepared individuals and are typically the number one threat/risk to a systems security. They are targeted through phishing methods and unless trained to know better can make an organization extremely vulnerable. The same goes for protecting their own data and maintaining their own privacy rights. Many users are simply uneducated about the tools available and how to use them. Thus, the other recommendation is to educate the public more broadly on how to preserve personal information on the Web.
Conclusions
Social networks are vulnerable to data harvesting algorithms and machine learning has only advanced the degree to which personal data may be gathered and interpreted for predictive purposes. The anticipated behaviors of users can be predicated upon past inferences by machine learning algorithms, but machine learning can also be used to protect the privacy of users and share only the information that the user is comfortable sharing. Machine learning protocols have even been developed that conceal data from Big Data harvesters. And yet there is no end in sight for this war over data, the conflict between what should be public and what should be private. Until legislation is passed putting an end to the debate, the war is likely to escalate.
References
Balle, B., Gascn, A., Ohrimenko, O., Raykova, M., Schoppmmann, P., & Troncoso, C. (2019, November). PPML\’19: Privacy Preserving Machine Learning. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (pp. 2717-2718). ACM.
Bilogrevic, I., Huguenin, K., Agir, B., Jadliwala, M., Gazaki, M., & Hubaux, J. P. (2016). A machine-learning based approach to privacy-aware information-sharing in mobile social networks. Pervasive and Mobile Computing, 25, 125-142.
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., … & Seth, K. (2017, October). Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1175-1191). ACM.
Hunt, T., Song, C., Shokri, R., Shmatikov, V., & Witchel, E. (2018). Chiron: Privacy-preserving machine learning as a service. arXiv preprint arXiv:1803.05961.
Lindsey, N. (2019). New Research Study Shows That Social Media Privacy Might Not Be Possible. Retrieved from https://www.cpomagazine.com/data-privacy/new-research-study-shows-that-social-media-privacy-might-not-be-possible/
Mohassel, P., & Zhang, Y. (2017, May). Secureml: A system for scalable privacy-preserving machine learning. In 2017 IEEE Symposium on Security and Privacy (SP) (pp. 19-38). IEEE.
Mooney, S. J., & Pejaver, V. (2018). Big data in public health: terminology, machine learning, and privacy. Annual review of public health, 39, 95-112.
Oh, S. J., Benenson, R., Fritz, M., & Schiele, B. (2016, October). Faceless person recognition: Privacy implications in social media. In European Conference on Computer Vision (pp. 19-35). Springer, Cham.
So, J., Guler, B., Avestimehr, A. S., & Mohassel, P. (2019). Codedprivateml: A fast and privacy-preserving framework for distributed machine learning. arXiv preprint arXiv:1902.00641.
Xu, K., Yue, H., Guo, L., Guo, Y., & Fang, Y. (2015, June). Privacy-preserving machine learning algorithms for big data systems. In 2015 IEEE 35th international conference on distributed computing systems (pp. 318-327). IEEE.
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