Holzinger, Andreas

Kieseberg, Peter

Malle, Bernd

Schrittwieser, Sebastian

TUGraz OPEN Library

ERCIM NEWS 107   October 201622Special theme: Machine LearningFigure1:Effectsofinformationremovalonselectedalgorithms.Data driven economy (and related con-cepts like Industry 4.0) and data drivenscience, as well as big data are the key-words most often heard in discussionson the future of high-profile industriesand on the upcoming revolutions in theeconomic world. With the integration ofmodern information technology into“classical” industrial environments orservices, many new opportunities can beenvisioned, e.g., in the optimisation ofsupply chains or in on-demand produc-tion of specifically tailored goods, buteven in governmental areas like healthenvironments, where P4-medicine (pre-dictive, preventive, personalised, partic-ipatory) is seen as a new paradigm thatcould revolutionise health care. With allthese new opportunities, the challengeswere traditionally located in the tech-nical area, especially regarding tech-nologies for enabling the efficient andcorrect analysis of the large amounts ofdata produced by factories and largesensor networks. In recent years, thearea of machine learning has seen asurge in new technologies developedand brought to the market. In combina-tion with the ever increasing amount ofcomputational power and storage that isavailable for a relatively reasonableprice, many of these applications cannow be applied in real life environ-ments.While many of the technological issueshave been apparently solved, the legalaspects of the collection and processingof vast amounts of data using machinelearning algorithms has been neglected(see [1]). The “right to be forgotten” hasbeen recently discussed with a partic-ular focus on removing personal dataand sensitive (personal) informationfrom automated analysis if requested byan individual. This brings up technicalas well as ethical questions. Especiallyin the European Union, the right to beforgotten has remained in political dis-cussion, especially fed by a legal basefor protection of personal informationon the Internet by the EuropeanCommission (see [L1]), with the draftEuropean Data Protection RegulationArticle 17 (see [2]). While the mainfocus in the current discussion isleaning mostly towards the removal ofinformation from search indexes ofprominent search engines like Google,the underlying technological challengesrun much deeper and touch fundamentalaspects of machine learning and itsapplication in the industry.One of the major questions are  theeffects of removing information fromknowledge bases on machine learningalgorithms. This is especially importantfor algorithms or analytical systems thatPrivacy Aware Machine Learning and the “Right to be forgotten”by Bernd Malle, Peter Kieseberg (SBA Research), Sebastian Schrittwieser (JRC TARGET, St.Poelten University of Applied Sciences), and Andreas Holzinger (Graz University of Technology)While machine learning is one of the fastest growing technologies in the area of computerscience, the goal of analysing large amounts of data for information extraction collides withthe privacy of individuals. Hence, in order to protect sensitive information, the effects of theright to be forgotten on machine learning algorithms need to be studied more extensively.ERCIM NEWS 107   October 2016 23rely on a large knowledge base con-taining previously analysed data tolearn from or which work by iterativelyenhancing the global analytical resultby continuously increasing the amountof information. Deletion of existingdata thus tends to worsen the quality ofthe existing results, putting the organi-sations operating under such law at aserious disadvantage compared toorganisations not subject to thoserestrictions. For instance, in developingclinical decision support software, com-panies not bound by the right to be for-gotten can train their algorithms on amore comprehensive data set, givingthem a worldwide advantage in mar-keting their product. In a project westudied the effects of selective deletionof valuable data items (in terms of theircontribution to a classifier accuracy) aswell as different levels of anonymiza-tion of a whole data set on machinelearning algorithms [3]. In those experi-ments, which were based on data fromthe 1994 US-census with around 32,000individual data records, the first phasetested four different classifiers (gradientboosting, linear SVC, logistic regres-sion, random forest) with respect to pre-cision, recall and F1-score.Subsequently, increasing fractions ofvaluable data were removed from thedata set, which resulted in significantloss of classifier performance. The other major research topic con-cerned the design of machine learningalgorithms and applications that cancope with anonymized or otherwisegeneralised information. The funda-mental idea is that the sensitive infor-mation is encoded by some privacy pro-tecting means, analysed using machinelearning algorithms and then preparedfor inspection. Although severalapproaches exist for this strategy, theyare currently not practical either due totheir impact on the quality of the results,or due to the additional costs intro-duced:• Trusted environments. While beingthe most popular strategy, the mainissue of using a trusted environmentlies in the large amount of resourcesthat have to be reserved for this task,even when the analysis is only donevery infrequently; e.g., using sharedenvironments like the Cloud is notpossible with this methodology. Fur-thermore, the environment needs tobe set up with high security standardsand a lot of audit and control mecha-nisms, as mechanisms for thwartinginsider attacks must be introduced,including fully trusted human opera-tors.• Anonymization. The data set is trans-formed into a derived set that blursthe sensitive attributes without actu-ally removing them altogether. Popu-lar techniques work by generalisingrecords until a certain minimalamount of them form an equivalencegroup (are indistinguishable).• Pseudonymization. Related toanonymization, Pseudonymizationworks by removing sensitive attrib-utes and replacing them with a place-holder, while keeping the internallogical structure of the data set, i.e.,records with the same sensitive attrib-utes get assigned the same pseudo-nym. • Functional Encryption. Functionalencryption allows the calculation ofcertain mathematical operations onthe encrypted values, e.g., let F(x) bethe encryption function of x and F-1 (y) the decryption routing, then itholds true that x+y=F-1 (F(x)+F(y)).While this works well in theory, cur-rently available algorithms are veryslow and thus cannot be used on closeto all real life scenarios.Our experiments to date (see [3] and[L2]) have focused on the loss of classi-fier performance when applied toanonymised knowledge bases. We usedthe same data set as described aboveand anonymised it using the k-anonymity criterion. The classifierswere then re-applied on a series ofincreasingly anonymised data sets (byincreasing the k-factor), again resultingin significant losses of classifier per-formance. A noteworthy differencebetween selective deletion andanonymisation of data is that classifierperformance on reduced data decreasedrather slowly at the beginning andbecame more drastic with increasedfractions of data removed, whereas foranonymised data sets the greatest lossoccurred instantly and subsequentlymellowed with increasing factors of k-anonymity (see Figure 1).In conclusion, we can see that theeffects of introducing the right to be for-gotten to machine learning predictablyresults in losses of algorithmic perform-ance. However, our experiments so farhave only considered classification. Anext logical step in our efforts would bethe inclusion of predictors as well asunsupervised learning methods (clus-tering for automatic label provision,pattern / preference recognition forproduct design etc.). Even though inreality the effects might not be as drasticas produced by our initial experimentalsetting, even a few percentage points inML performance could make a signifi-cant difference in crucial areas of appli-cation or highly competitive marketenvironments. To sum up, we believe alot of additional future research isneeded in order to: • fully understand the effects of theright to be forgotten on machinelearning environments;• be able to design algorithms moreresilient to changes in the knowledgebase;• understand the effect of perturbingother forms of knowledge bases, e.g.,graph based data sets, in which dis-tance is derived from node featurevectors as well as associations.Links:[L1] http://ec.europa.eu/justice/data-protection/document/review2012/com_2012_11_en.pdf[L2] http://www.hci-kdd.org/ References:[1] P. Kieseberg, H. Hobel, S.Schrittwieser et al:  “ProtectingAnonymity in Data-Driven BiomedicalScience”, pp. 301-316, 2014.[2] P. De Hert, V. Papakonstantinou :“The proposed data protectionRegulation replacing Directive95/46/EC: A sound system for theprotection of individuals”, ComputerLaw & Security Review 28, no. 2(2012): 130-142, 2012.[3] B. Malle, P. Kieseberg, E. Weippl,A. Holzinger: “The Right to BeForgotten: Towards Machine Learningon Perturbed Knowledge Bases”,Workshop on Privacy Aware MachineLearning (PAML), August 2016Please contact:Peter KiesebergSBA Research, Vienna, Austriapkieseberg@sba-research.org

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Privacy Aware Machine Learning and the Right to be forgotten

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